Recent advances in multimodal learning have enabled powerful Vision–Language–Action (VLA) systems for robotic reasoning and control. However, most existing approaches rely on Transformer backbones, which face scalability and efficiency bottlenecks for long sequences. This work introduces MambaVLA, a scalable VLA framework built on the Mamba state space architecture for efficient sequence modeling. The framework integrates the Eagle visual encoder and Qwen-7B-Chat-Int4 language model to achieve fine-grained multimodal fusion with linear-time complexity. A diffusion flow matching module further aligns visual–language embeddings with continuous action trajectories, enabling smooth and precise control. Extensive evaluations on standard VLA benchmarks demonstrate that MambaVLA matches or surpasses Transformer based models while offering substantially lower computational cost and faster inference. These results highlight the potential of state space modeling and flow-based action generation for compact, scalable, and deployable embodied intelligence systems. https://sainavaneet.github.io/MambaVLA.gihub.io/

This article studies the state estimation problem for singularly perturbed semi-Markov jump coupled neural networks. To save bandwidth resources and reduce communication frequency, a weighted try-once-discard protocol is recommended. The protocol prioritizes the transmission of node information with the largest weighted error, thereby enhancing the efficiency of information exchange within the network. At the same time, in the process of information interaction, a new encryption-decryption scheme is designed to effectively prevent the leakage of sensitive information while ensuring ideal estimation performance. For the singular perturbation parameter in the coupled neural network, this article uses the matrix contract transformation method to strictly realize the separation of the two dynamics of the system, thereby ensuring that the established mean square exponential stability criterion can be applied to the augmented system. Lastly, numerical simulations are employed to confirm the validity of the provided state estimator.

In this paper, the challenging problem of prescribed-time (PT) adaptive intelligent tracking control is investigated for the nonlinear uncertain systems with external disturbance under sensor deception attacks. Firstly, since the existence of sensor deception attacks, the states and output of the systems in the network channel are unavailable. To compensate for the attack gains and achieve PT tracking control under deception attacks, we substitute the gains generated by deception attacks and the PT adjustment function into the designed coordinate transformation, and apply the Nussbaum function technique to process the attack gains in the controller design process. Secondly, based on the cited definition of the PT adjustment function, a novel stability criterion is proposed and proven. Then, utilizing the available compromised states, the corresponding PT adaptive intelligent tracking controller is constructed using backstepping method, and the fuzzy logic system (FLS) is applied in the design process to approximate the unknown continuous function, avoiding the “complexity explosion” problem caused by repeated differentiation and thus reducing the computational burden to a certain extent. Further, according to the proposed stability lemma, it is demonstrated that the designed control strategy is capable of enabling the systems output to achieve zero-error tracking within the prescribed time considering external disturbances and deception attacks. At last, the effectiveness of the designed control scheme is illustrated by MATLAB simulation results.

This paper delves into the issue of exponentially asymptotically dynamic feedback stabilization of switched systems with aperiodic sampled-data, taking limited communication capacity into account. Bearing the upper bound on communication bit rates in mind, an event-triggered mechanism, which rules out Zeno behaviours, is put forward to determine the sampling instants and the transmission bits with a lower bound that ensures asymptotic stability at a prescribed convergence rate for switched systems. The control strategy, which not only aims to minimize the number of transmissions but also takes the limited communication capability into account, is an emulation approach where the controller gains have been fixed in advance and the event-triggered rules have to be designed. The effectiveness of the proposed approach is evaluated on the presented simulations and its applications in single-link robotic manipulators.

The stabilization issue of noisy sampled-data networked control systems in the presence of deception attacks and noisy sampling intervals is investigated in this paper. Firstly, networked control systems with deception attacks and noisy sampling intervals are acquired under a consistent structure. Next, feedback interconnection model with zero initial state is established, which facilitates the use of the stochastic discrete-time integral quadratic constraint. Uniform exponential stability conditions are given for networked control systems related to operators expectations with stochastic properties. Subsequently, the difficulties associated with the nonlinear term are overcome and a novel controller algorithm that enables uniform exponential stability of the networked control systems is designed. Lastly, the effectiveness of the designed algorithm is validated by numerical example and direct current motor simulation example.

This paper proposes a novel two-stage reinforcement learning (TSRL) scheme to address the linear quadratic tracking (LQT) control problem of discrete-time interconnected systems (DTISs), commonly encountered in Internet of Things (IoT) applications. The proposed approach consists of two main stages: i) the derivation of an admissible control policy, ii) the computation of the optimized tracking control policy. In the first stage, regulation parameters are introduced, and a scaling technique is employed to construct an artificially stable system. By gradually decreasing the regulation parameter, an admissible control policy is obtained that makes the original system Schur stable. In the second stage, the original system is augmented, and a discounted performance index is introduced, thereby transforming the LQT problem of interconnected systems into the solution of a discrete-time discounted Lyapunov equation. Based on the admissible control policy obtained in the first stage, an optimized tracking control policy is further derived. The proposed tracking controller design method eliminates the requirement for the complete system dynamics and an initial stable control policy. Finally, a numerical example and a networked DC microgrid system are provided to demonstrate the effectiveness and practicality of the proposed method.

This paper proposes a fuzzy optimal tracking control scheme for networked nonlinear Markovian switching systems under an event-triggered mechanism. An augmented system is constructed by integrating system dynamics with the reference signal. Then, an actor-criticQ-learning method achieves adaptive control without requiring prior knowledge of system matrices or transition probabilities. An event-triggered mechanism is introduced to update control actions on demand, significantly reducing network communication load while preserving tracking performance. An online actor-critic neural network algorithm is designed. The critic network approximates the value function, and the actor network updates the control policy in real time, enabling collaborative optimal tracking control. Theoretical analysis shows that the resulting neural network-driven closed-loop system exhibits bounded weight estimation errors and is uniformly ultimately bounded. Simulations performed on a tunnel diode circuit model confirm the effectiveness and resource efficiency of the proposed method in a networked environment.

This paper develops an optimal leader-following consensus protocol for a second-order nonlinear multi-agent system with disturbance via dynamic event-triggered sliding mode strategy. The proposed control protocol contains two dynamic event-triggered mechanisms, which are used to regulate the updates of the discontinuous control and optimal consensus control parts. Considering the effect of disturbance, a novel distributed event-triggered sliding mode control protocol is developed to ensure the predefined-time reachability of the sliding surface and yields sliding mode dynamics that are robust to external disturbances. To further obtain the optimal event-triggered consensus protocol, an online critic neural network is designed with weight updated by the concurrent learning method. Then, the asymptotic stability of sliding mode dynamics and uniform ultimate boundedness of the weight estimation errors are proved based on the Lyapunov method. Finally, the effectiveness of the proposed method is verified by simulations of multiquadrotor unmanned aerial vehicle models.

This paper investigates the security issue of malicious adversaries undermining the remote estimator over cyber-physical systems (CPSs). A critical-data-oriented (CDO) attack strategy is proposed to enable attackers to utilize essential data judiciously during data transmission, thereby degrading remote state estimation maximally. In addition, the proposed scheme is compared with commonly studied attack strategies such as Bernoulli-distributed and periodic denial-of-service (DoS) attacks within a unified framework. The evaluation of its impact focuses on two factors: the weighting matrix (WM) and the residual information. Further, the consideration of attack energy distribution (AED) and quality of service (QoS) constraints is merged. The theoretical analysis based on stochastic methods reveals the inherent associations among WM, attack energy, and signal-to-interference-plus-noise ratio (SINR). The attack impact on channel throughput is analyzed and formulated as a convex optimization problem. The extended part explores the selection of optimal WM under special scenarios. Finally, two examples are established to validate the correctness and practicality of the concerned CDO attack strategy.

This paper investigates an event-triggered security control strategy for networked autonomous ground vehicles against actuator attacks. Firstly, a novel bandwidth-aware average-event-triggered protocol is proposed to improve the usage efficiency of limited network resources. There are two main merits of this protocol, the one is that the average of historical data is used to mitigate the over-triggering resulted from random data jitter, the other is that a bandwidth-aware dynamic triggering threshold is constructed to increase the adaption ability of protocol to the time-varying bandwidth status. Secondly, in order to implement state feedback control, a dynamic observer based on a dynamic variable related to the observation error is presented to enhance the state estimation accuracy. Thirdly, a new attack estimator relying on hyper basis function neural network is devised to estimate the unknown malicious attacks injected into the actuator. By combining the observed state and estimated attack, an attack-compensation-based security control strategy is developed to mitigate the negative influence caused by actuator attacks and further increase control performance. Then, by using Lyapunov theory and linear matrix inequality techniques, several sufficient criteria to calculate the controller and observer matrices are derived. Finally, the validity of the addressed strategy is verified via some simulation outcomes.

This paper investigates the secure optimal load frequency control (LFC) problem for Markovian multi-area power systems under false data injection (FDI) attacks. First, by employing the Markov jump process to describe the power system’s state changes induced by FDI attacks, a Markovian multi-area power system model is constructed. Subsequently, considering the secure optimal LFC design of the Markovian multi-area power system, coupled Riccati equations (CREs) are derived. Then, to solve these CREs, an adaptive weight-dependent iteration algorithm is proposed. By progressively increasing the weight of the latest step information, the algorithm can accelerate convergence. Finally, the effectiveness of the secure LFC is validated through a simulation example of a Markovian three-area power system. Compared with the fixed weight iteration algorithm, the proposed adaptive weight-dependent iteration algorithm exhibits faster convergence and lower residuals.

This article investigates the data-driven control problem of linear time-invariant (LTI) multi-input systems under aperiodic sampling, where the input of the system is determined by the corresponding agent. Focusing on partially unknown system models, we provide a methodology that integrates Integral Quadratic Constraints (IQC) with dissipation inequalities to derive the asymptotic stability criterion and controller design method for such systems. Firstly, addressing the challenge of incomplete system model knowledge, this article develops adaptive system identification algorithm for the above two scenarios, respectively. Each algorithm facilitates accurate model reconstruction directly from the available data. Then, the feedback interconnection between the LTI multi-input system and time-delay operator is established, and the asymptotic stability criterion for the system is demonstrated. In addition, by means of matrix decoupling techniques, a restricted frequency-domain data-driven framework which can effectively address complex coupling dynamics and overcome structural perturbation constraints is proposed, along with two corresponding controller algorithms. Finally, through the simulation analysis and result verification of numerical examples, the effectiveness of the proposed methods are demonstrated.

Euler-Lagrange multi-agent systems (MASs), which are typical IoT systems, have wide applications including autonomous underwater vehicles, mobile robots and robot manipulators. As a crucial aspect of Euler-Lagrange MASs, the consensus becomes a research priority. However, most of the existing works on consensus of Euler-Lagrange MASs are based on the assumption of linearized parameters, and the designed distributed event-triggered (ET) control protocols are only applicable to the leader-following consensus (LFC) of uncertain Euler-Lagrange MASs with undirected graphs. Hence, the Leaderless consensus (LLC) of uncertain Euler-Lagrange MASs with digraphs is studied in this paper. Based on fuzzy logic system (FLS), a fully distributed ET adaptive control protocol is designed, and new theoretical analysis results are proposed in Lemmas 3–5. By the proposed fully distributed ET fuzzy control protocol, the communication resources are effectively saved, and the LLC for uncertain Euler-Lagrange MASs without the assumption of linearized parameters is achieved. In the end, based on networked two-linked robot manipulators, simulations are given to show the validity of the proposed results. In comparison with the method of [28], our method with σi= 0.005 provides 263.48%, 245.87%, 237.15%, 210.46% and 210.72% less information exchange on manipulators 1-5, respectively.

We propose a novel post-processing approach for the local optimization of Locally Optimized RANdom SAmple Consensus (LO-RANSAC), called the Multi-Estimation-based Parameter Centroid (MEPC) decision. It is observed that the optimal thresholds for hypothesis generation and evaluation differ in local optimization with the inner RANSAC. Instead of binary labeling for inliers and outliers, a new ternary labeling for inliers, midliers, and outliers is introduced, using two thresholds. Our experimental results show that the highest-scoring model measured by the ternary method is closer to the real model than that measured by the existing binary method. However, it should be noted that the highest score still does not correspond to the best model due to inaccurate evaluation by data noise. We introduce a new linear model centroid decision method to compensate for the highest-scoring model distorted by noise. In this process, an efficient method for measuring the similarity between two hypotheses is introduced, and candidates close to the real model are found by comparing their similarity with the highest-scoring model. Our approach determines a representative model of the multiple candidate hypotheses, which is defined as the geometric centroid of hyperplanes. We test on various datasets for homography, fundamental, and essential matrices, demonstrating that applying MEPC to existing RANSAC algorithms achieves more accurate and stable model estimation. Moreover, additional experiments on vanishing point detection show the potential of our approach for various model estimation applications.

To address the dual challenges of tracking accuracy and resource efficiency for quadrotor unmanned aerial vehicle (UAV) attitude control, an event-triggered based control strategy combining backstepping sliding mode control (BSMC) with a linear extended state observer (LESO) is proposed. With a quadrotor UAV dynamic model established via Euler-Newton equations, a BSMC which combines sliding mode control and backstepping is proposed to guarantee stability of UAV and a LESO is used for real-time compensation of total disturbances to improve tracking accuracy. Moreover, a time-varying threshold event-triggered (TVST) mechanism is also deliberately developed to reduce controller update frequency while maintaining the system performance. The proposed methed is an emulation one which significantly reduces the communication and computation burden and avoid the unnecessary wear and tear of the actuator. Simulations and experimental results demonstrate that the proposed control strategy achieves an effective balance between tracking accuracy and resource conservation.

This paper addresses the fault estimation and compensation control problem for nonlinear stochastic multi-delays systems with multi-source faults. The T–S fuzzy model is used to describe the nonlinearity of the system. By assuming that each stochastic delay is independent and follows the Bernoulli distribution to describe the stochastic multi-delays possibly caused by sensor/actuator faults, the targeted fault observers with historical information are introduced to estimate the sensor/actuator faults respectively, so that the designed fault-tolerant compensation control (FTCC) method can ensure the stochastic stability andH∞control performance for the closed-loop FTCC system. The sufficient design conditions for the fuzzy observer and the fault-tolerant compensation controller are obtained through the application of Lyapunov-Krasovskii stability theory and techniques. Finally, the effectiveness of the designed algorithm is verified through a common automation control system.

This article addresses the data-driven asynchronous dynamic event-triggeredH∞tracking control problem for singularly perturbed systems (SPSs) with unknown slow dynamics and unknown bounded disturbances. First of all, considering the two-time-scale characteristic of SPSs, anH∞tracking control problem for the slow subsystem and an asymptotic stability problem for the fast subsystem are formulated via time-scale decomposition. Secondly, an asynchronous dynamic event-triggered scheme (ETS) based on dual-rate sampling is proposed to reduce the communication burden. Then, a data-based parameterized model of the augmented system is given, which consists of the reference system and the slow subsystem with unknown dynamics. Further, by combining the data-based parameterized model and employing the full-block S-procedure, the data-based co-design method of tracking controller and event-triggered matrix is developed. The overall stability analysis of the full system under the composite controller is given. Finally, the proposed scheme is verified by a Chua’s circuit and a networked DC motor control system.

This article addresses differentially private average consensus for heterogeneous multi-agent systems under aperiodic sampled-data control. A distributed hybrid dynamic controller based on a decomposition-compensation approach is proposed. Individual privacy is safeguarded by integrating non-decaying Laplacian noise into interaction information and decomposing the reference state into interactive and reserved substates, preventing direct exposure of true information to eavesdroppers. Leveraging the Robbins-Monro conditions, a time-varying step-size is designed to relax noise constraints, accelerate convergence, and achieve asymptotically unbiased output average consensus in mean-square sense. Finally, a numerical simulation and a physical experiments involving mobile robots and unmanned aerial vehicles validate the efficacy of the proposed scheme.

This article investigates the design of an adaptive fuzzy output-feedback decentralized controller for a class of nonlinear multiagent systems (MASs). The considered systems are subject to unmeasurable states and are vulnerable to denial-of-service (DoS) attacks. Unknown nonlinear functions are approximated using fuzzy logic systems (FLSs). Furthermore, a switching-type fuzzy state observer is introduced to estimate the unmeasurable states. By leveraging the adaptive backstepping design technique and the dynamic surface control method, an adaptive fuzzy output feedback decentralized controller is synthesized. By combining Lyapunov stability theory with the concept of average dwell time (ADT), we rigorously prove that the proposed adaptive fuzzy decentralized resilient controller ensures the convergence of tracking errors to a small neighborhood of the origin despite DoS attacks, while maintaining the semi-global uniform ultimate boundedness (SGUUB) of all closed-loop signals. The effectiveness of the control strategy is verified by a nonlinear inverted pendulum system model and a group of multinonholonomic mobile robots.

The study of dynamics in complex systems has increasingly incorporated higher order interactions, which capture the collective influence among three or more units, extending beyond traditional pairwise connections. Although such interactions are observed in biological neural networks, their precise role in shaping network dynamics and the feasibility of controlling these dynamics remain unclear. This article proposes a controlled diffusion hub neural network model that explicitly includes higher order interactions. To regulate the resulting spatiotemporal dynamics, a cross-node associated delayed feedback control (CNADFC) method is further introduced. Our analysis establishes conditions for local stability, Turing instability, and Hopf bifurcation. We show that while Turing instability cannot arise, spatially periodic patterns emerge under specific parametric conditions. Numerical simulations confirm these theoretical findings and highlight the pronounced effects of self-feedback, control, and first-order interaction on stability and dynamic behaviors; in contrast, higher order interactions exert a comparatively modest influence. Furthermore, simulations illustrate how the CNADFC method can effectively optimize spatiotemporal dynamics. This work advances the understanding of diffusion neural network behavior under complex higher order interaction and provides a reference for the effective control of such networks.

In this study, the observer-based asynchronous stabilization is addressed for networked system under multichannel attacks, in which the asynchronous phenomenon refers to the mismatch between the controller mode and the actual attack mode. To accurately depict complex attack behaviors, a piecewise homogeneous semi-Markov chain (SMC) model modulated by a superstratum Markov chain is introduced, which can simultaneously describe the randomness of attack mode transitions and the time-varying nature of transition probabilities. Considering that the actual attack modes are inaccessible, an observer-based mode switching delay technique is designed to solve this challenge. Under the framework of a piecewise homogeneous SMC, a sufficient criterion is established to ensure the $\varsigma $ -error mean-square stability under random multichannel denial-of-service attacks by means of a Lyapunov function depending on observed attack modes, piecewise homogeneous variables, and elapsed time. Moreover, matrix decoupling and convexification techniques are employed to reduce the computational complexity. Finally, the effectiveness of the proposed method is demonstrated through two practical simulation cases.

This article addresses the optimal state observation and leader-following consensus for a nonlinear multiagent system (MAS) with input saturation under the Stackelberg game framework. The dynamics and states of followers are unknown, the leader's dynamics is unknown, and the leader's state is accessible only to a subset of followers. First, a distributed estimation algorithm is developed for each follower to estimate the leader's state. Then, a game-based observer is designed to estimate the follower state, where the bidirectional interaction between the observer and follower dynamics is considered. The follower dynamics and observer are modeled as leader and follower players in the Stackelberg game, respectively. Based on the proposed structure, an optimal auxiliary controller for the observer and an optimal consensus controller are developed. Furthermore, a fuzzy reinforcement learning approach approximates the unknown dynamics and derives the optimal state observers and leader-following consensus controllers. All closed-loop signals are guaranteed to be uniformly ultimately bounded based on the Lyapunov method. Finally, simulations are provided to validate the effectiveness of the proposed approach.

The optimal tracking control problem for multiplayer differential game systems (MDGS) with unknown dynamics is investigated in this article. A two-stage asynchronous learning scheme is proposed to achieve Nash equilibrium solutions without requiring initial admissible control policies. In the first stage, stabilizing control policies are constructed through a homotopic-based iterative process. In the second stage, an asynchronous policy iteration (PI) method is employed, in which players sequentially update their policies using partial real-time information, contributing to improved convergence efficiency compared to synchronous approaches. The proposed scheme is further extended to a data-driven framework, relaxing the requirement of explicit system dynamic information. Convergence under stabilizability and detectability conditions is theoretically proven. Finally, two simulation examples are conducted to demonstrate the effectiveness of the proposed method in tracking a sinusoidal reference. Additionally, comparison experiments are provided to highlight the superiority of the proposed algorithm.

This paper addresses the fault detection and isolation (FDI) problem for discrete-time Lipschitz nonlinear systems represented by Takagi-Sugeno (T-S) fuzzy models. A zonotopic set-membership estimation framework is proposed to robustly handle system uncertainties and bounded disturbances. First, an$H_\infty$observer is designed based on linear matrix inequality (LMI) conditions to ensure robust performance. The estimation error is bounded within a recursively propagated zonotope, where generator growth is controlled via zonotope order reduction. Second, a fault detection scheme is developed by constructing residual zonotopes and checking whether the residual lies within the associated bounding boxes. Third, to achieve fault isolation, two augmented observers are designed, respectively addressing actuator fault isolation and sensor fault isolation. For actuator fault isolation, a state-augmented observer is proposed to eliminate the influence of sensor faults in the residual. For sensor fault isolation, a disturbance-augmented observer is constructed to decouple the actuator fault effect. In both cases, zonotopic residual bounds are derived to detect and isolate the fault source. Finally, simulation results on a fuzzy nonlinear system illustrate the effectiveness and robustness of the proposed fault detection and isolation approach.

This paper studies the distributed optimal strategy problem in multi-agent pursuit-evasion game for capture and formation control under false data injection attacks (FDIAs) and input saturation constraints. In the game, pursuers aim to approach evaders while evaders attempt to keep a distance, and agents within the same team are required to preserve cohesion. To achieve control objectives under the FDIAs and input saturation constraints, a hierarchical control framework is developed, which includes a sliding mode control (SMC) layer and an optimal control layer. Moreover, the SMC strategy is designed to mitigate the effects of FDIAs and input saturation. Then, since the sliding mode dynamics involves coupling terms associated with formation displacement, a fuzzy reinforcement learningbased optimal control strategy is proposed to achieve capture and formation aims. Furthermore, based on the Lyapunov method, it is proven that all closed-loop signals are uniformly ultimately bounded. Finally, the effectiveness of the proposed method is validated by simulations conducted on distributed mobile robots.

This paper focuses on the T-S fuzzy output path tracking control issue for variable speed autonomous ground vehicles (VSAGVs) by using state observer and event-triggered mechanism. The model of VSAGVs with nonlinear dynamics and parameter uncertainty is represented by the uncertain T-S fuzzy system. To enhance the estimation performance of the unknown system state, a fuzzy proportional- integral observer containing an extra integral term of estimation error is designed. To reduce the network communication burden, a novel deep-Q-learning-based proportional- integral event-triggered mechanism by adding the integration of the normal triggering rule and introducing an optimization strategy of the triggering threshold. Compared with the classical normal and dynamic event-triggered mechanisms, a larger inter-event time can be generated by the proposed proportional- integral event-triggered mechanism. In addition, the deep-Q-learning-based optimization strategy is able to dynamically regulate the triggering rate according to the historical triggering performance and control performance. Some sufficient conditions are derived to solve the fuzzy observer and controller gains and the triggering matrix such that the VSAGV is asymptotically stable with given$H_{\infty }$level. Finally, the superiorities of the addressed approach are confirmed through some simulation results.

This article focused on the investigation of boundary dissipative control for a class of nonlinear parabolic partial differential equation (PPDE). By employing the Takagi–Sugeno (T–S) fuzzy modeling framework, the nonlinear system is characterized as a category of linear PPDE. An output-based nonparallel distributed fuzzy state observer is developed to estimate the unmeasured system state. Dynamic errors with undetermined parameters for the PPDE subject to appropriate boundary conditions are presented. To overcome performance limitations of traditional controllers in managing systems with spatially distributed characteristics, a boundary-based nonparallel distributed dynamic control scheme incorporating time delays and actuator failures is developed, building upon the established observer framework. Meanwhile, to enhance the efficiency of network resources, signals of the measurement output, observer state, and controller state are quantized through a specific dynamic quantizer scheme. Then, the finite-time dissipative performance of the fuzzy system is rigorously analyzed through the constructed Lyapunov functionals. Conditions and parameters design algorithm for the fuzzy closed-loop systems are established to ensure the specified control performance. In addition, a method involving arbitrary given matrices is employed to decouple the established nonlinear conditions. Finally, the efficacy of the proposed dissipative control strategy is validated through simulation results of the hot strip mill cooling process.

This work addresses the dissipative synchronization for complex dynamical networks (CDNs) with semi-Markov jump parameters under adaptive event-triggering. Due to the complexity of network environment, a semi-Markov process (SMP) is introduced into the system modeling. The adaptive event-triggering protocol (AETP) is developed to dynamically adjust the triggering threshold based on the synchronization error, which significantly reduces unnecessary data transmissions and avoids Zeno behavior by guaranteeing a positive inter-event interval. Subsequently, a nonfragile controller is designed for potential fluctuations in controller gain. By constructing Lyapunov–Krasovskii functional (LKF) and employing advanced integral inequalities, sufficient synchronization conditions are derived to ensure that the CDNs achieve stochastic stability with strictly dissipative characteristic. Simulation results demonstrate the effectiveness of the proposed control scheme.

This article presents a novel reinforcement learning (RL)-based load frequency control (LFC) algorithm designed for Markov jump multiarea interconnected power systems (MJMIPSs). Existing LFC methods often require precise dynamic information of the power system, which is challenging to acquire accurately due to the the system complexity, stochastic disturbances, and high modeling costs. Such limitations make it difficult to implement effective control strategies in real-world applications. In response to these challenges, we propose a decentralized hybrid iteration (HI) algorithm that combines the RL scheme with a decentralized control technique to address the LFC problem for MJMIPSs. In contrast to conventional RL schemes, such as policy iteration (PI) and value iteration (VI), the proposed algorithm achieves the controller design without subsystem decomposition (SD) by employing mixed-mode data acquisition and mode-related data classification, while eliminating the requirements on exact system dynamics and initial admissible control policies. Finally, we verify the effectiveness of the proposed method through the power systems.

In this paper, a resilient distributed model predictive control (DMPC) algorithm is proposed for the networked vehicle platoon system under denial-of-service (DoS) attacks. It is difficult to ensure the system-level stablibity and string stability of the networked vehicle platoon system simultiniously in the presence of DoS attacks. To this end, an optimization problem is established, which is related to the trajectories of the system. To minimize the cost function, the last-step shifting technique is employed and the assumed solution is determined by the local optimal solution. The last optimal solution without the occurrence of DoS attacks is used as the reference trajectory. This reference trajectory is then transmitted to the interconnected vehicles within the system. Demonstrating the stability of the closed-loop platoon system is achieved by using the Lyapunov function, which is designed by the sum of the cost function. String stability of the platoon system, the unique characteristic of platoon systems, is proven through rigorous mathematical derivation. Compared with existing results, the vehicle platoon system under DoS attacks can be converged both the internal and closed-loop stability. Finally, several simulation and experimental results are presented to verify the effectiveness of the proposed method.

The H∞ reduced-order filtering problem for nonlinear semi-Markov jump systems is investigated in this work. To tackle the nonlinearity of the plant, the Takagi-Sugeno fuzzy approach is adopted. Considering the limited network resources, the logarithmic quantizer and the weighted try-once-discard protocol are used to relieve the communication load for networks and avoid data congestion. Then, the reduced-order filter is constructed by combining the processed data and the designed slack matrix. On the basis of the fuzzy-model-based method and the Lyapunov theory, some criteria are established to ensure that the filtering error system is mean-square exponentially stable. Finally, the superiority and validity of the results obtained in this work are proved by two illustrative examples.

Event-driven fault detection of networked photo-voltaic microgrids usually requires accurate measured data. In real engineering environment, stochastic fluctuating measurements caused by system disturbances, measurement noises and outliers are inevitable. However, existing event-driven mechanisms using single measured data are sensitive to such stochastic fluctuations, which could lead to redundant events induced by disturbances and noises, and false events and fault alarms resulted from measurement outliers, respectively. In order to solve this problem, a novel bandwidth-aware and anti-outlier fusion-event-driven mechanism is proposed for the first time. First, to cut down the redundant events, the fusion of historical measurements is utilized to reduce the stochastic fluctuations and smooth the measurements. Second, an extra upper threshold constraint is introduced to exclude the false events and fault alarms. In addition, a dynamic triggering threshold relying on the bandwidth status and the fused measurements is constructed to improve the mechanism’s flexibility to complex and time-varying network situation. Then, the closed-loop event-triggeredH∞fault detection system with transmission delays is modeled as a time-varying delay system. With the help of Simpson rule, some sufficient conditions formulated by linear matrix inequalities are derived for co-designing the residual filter gain of fault detector and the triggering matrix. Finally, some simulation outcomes are presented for demonstration of the theoretical claims.

This paper investigates the fully distributed optimization control problem for high-order nonlinear multi-agent systems (MASs) with unknown nonlinearities. Initially, a fully distributed optimal signal estimator with adaptive gains is developed, which ensures that its output asymptotically converges to the optimal solution of the optimization problem without requiring any global information. Subsequently, the distributed optimization problem is transformed into a reference-tracking problem. By employing backstepping technique, a distributed control protocol is designed for each agent, enabling the outputs of all agents to asymptotically track the estimator’s output. It is shown that the boundedness of all signals in closed-loop systems and the distributed optimization objective can be successfully achieved. Furthermore, to reduce continuous communication among agents, the optimal signal estimator is redesigned by introducing a dynamic event-triggered mechanism (DETM), making it not only a fully distributed algorithm but also one that minimizes energy consumption. Finally, the effectiveness of the algorithm is validated through numerical and practical simulations.

This article investigates the problem of adaptive fixed-time optimal consensus tracking control for nonlinear multiagent systems (MASs) affected by actuator faults and input saturation. To achieve optimal control, reinforcement learning (RL) algorithm which is implemented based on neural network (NN) is employed. Under the actor-critic structure, an innovative simple positive definite function is constructed to obtain the upper bound of the estimation error of the actor-critic NN updating law, which is crucial for analyzing fixed-time stabilization. Furthermore, auxiliary functions and estimation laws are designed to eliminate the coupling effects resulting from actuator faults and input saturation. Meanwhile, a novel event-triggered mechanism (ETM) that incorporates the consensus tracking errors into the threshold is proposed, thereby effectively conserving communication resources. Based on this, a fixed-time event-triggered control scheme grounded in RL is proposed through the integration of the backstepping technique and fixed-time theory. It is demonstrated that the consensus tracking errors converge to a specified range in a fixed time and all signals within the closed-loop systems are bounded. Finally, simulation results are provided to verify the effectiveness of the proposed control strategy.

This article investigates prescribed performance neural autonomous control of discrete-time nonlinear systems subject to actuator saturation. Unlike existing sliding-mode-control (SMC) driven structure, we aim to construct a more general and user-friendly framework that achieves prescribed behaviors by indirectly stabilizing transformed errors in the discrete-time domain. We propose two new approaches to accomplish such objective. The first one is to compensate actuator saturation by developing a modification system, the state of which is further applied to design flexible terms that endow an ability to resiliently adjust prescribed envelopes according to saturation condition, which constitutes the second approach. In comparison with current discrete-time prescribed performance control (PPC), the proposed method avoids the singularity problem and consequently remedies the fragility defect. In addition, an adaptive neural back-stepping procedure is used to devise discrete-time PPC protocols that limit tracking errors inside the developed flexible prescribed envelopes to satisfy both transient and steady-state properties. Finally, the efficiency of design is illustrated via Lyapunov synthesis, and is further verified by numerical simulation.Note to Practitioners—The motivation arises from the requirement for autonomous tracking control of discrete-time nonlinear systems with actuator saturation and fragility relaxation. Unfortunately, existing SMC-driven discrete-time PPC isn’t able to achieve expected prescribed behaviors in the presence of actuator saturation. To break through this bottleneck, we firstly develop a new design framework, being different from existing SMC-based ones, to impose prescribed performance on tracking errors by indirectly stabilizing transformed errors, and then we define a new modification system which effectively compensates the actuator saturation so that the fragility defect can be remedied. On this basis, a novel discrete-time tracking control approach with flexible prescribed performance is addressed for constrained systems while relaxing the fragility. The obtained results in this study are of great significance for providing a more general and user-friendly framework for discrete-time PPC synthesis.

A security control problem is addressed for a kind of networked Markov jump system subject to two-side stochastic denial-of-service (DoS) attacks, in which the attack occurrence situation obeys the Markov chain. To resist the attack effects, a redundant channel protocol is adopted, wherein measurement/control signals are sent to the controller/actuator via the redundant channel when the primary channel suffers from cyber-attacks. According to the above redundant channel protocol, both measurement and input models are established to represent the received signals by the controller and actuator, respectively. Then, a novel two-layer mapping strategy via logical operations is proposed to describe attack occurrences and system jumping. These facilitate the design of a security sliding mode controller, under which the exponential mean-square stability of the networked Markov jump systems subject to DoS attacks and the corresponding sufficient conditions are derived. Eventually, the simulation results via the DC chopper circuit system are provided to illustrate the proposed redundant-channel-based security control method.Note to Practitioners—This paper is motivated by the attack resistance problem in networked control systems. With the revolutionary evolution of wireless communication technology, in many practical systems, such as smart power grids, unmanned aerial vehicles (UAVs), and unmanned surface vehicles (USVs), the physical devices including sensors, actuators, controllers, and other intelligent devices are interconnected and transmitted the data through a network infrastructure. The inevitable DoS attacks may cause reliability degradation of data transmission, which in practice results in shaky grid frequency, failed UAV formation, and inaccurate USV detection. Distinguished from the previous investigation results from a passive perspective, this paper proposes a redundant-channel-based method to solve the reliability degradation problem caused by DoS attacks from a proactive perspective. The developed redundant channel method against Markov-chain-based DoS attacks can improve the reliability of data transmission, and the proposed novel two-layer mapping strategy via logical operations can increase the freedom of security controller design. A DC chopper circuit is used to verify the effectiveness of the proposed novel control method and comparison simulation results show the advantages of the redundant channel method. How to improve the reliability of vulnerable channels is a knotty problem in communication. With the groundbreaking research of this paper, against other attacks, such as deception attacks, reply attacks, and frequency-and duration-constrained DoS attacks, the attack resistance problems via proactively strengthening the reliability of data transmission should be further explored in the future.

This paper addresses the problem of adaptive neural network sliding mode control for sampled-data singularly perturbed systems subjected to randomly perturbed sampling periods and unrestricted deception attacks. In contrast to the conventional transition probabilities,the utilization of sojourn probabilities are essential to capture the inherent randomness associated with the sampling periods. Furthermore, in the presence of unrestricted deception attacks, a neural network-based approach is utilized to estimate and mitigate their impact on system performance. Additionally, a sliding mode controller is developed based on both the sampling period mode and the singularly perturbed parameter. This controller ensures the exponential ultimate boundedness in the mean square sense and the reachability of the specified sliding surface for the closed-loop system. Finally, the proposed theory is validated through a practical example, demonstrating its effectiveness and soundness. Note to Practitioners — Due to the advent of the digital era, periodically sampled-data systems have received much attention. However, practical engineering conditions often introduce non-ideal factors, such as network congestion, noise interference, and voltage instability, which can cause randomly perturbed sampling. As considered in this paper, a sojourn probability, which is more easily obtained by statistical methods, is applied to model the variation of sampling periods. On the other hand, in real network environments, the boundedness of an attacker’s injection attack cannot be guaranteed. Therefore, in this paper, we consider using a neural network-based approach to estimate and mitigate their impact on system performance in the presence of unrestricted deception attacks. Meanwhile, sliding mode control, which has drawn widespread attention for its cost-saving and reliability, is utilized to achieve exponential ultimate boundedness in the mean square sense and reachability of the specified sliding surface of the controlled system.

This study focuses on the learning-based asynchronous sliding mode control for switching systems, operating under a general switching rule and partially unknown probability information. A novel switching rule is constructed, governed by a joint probability distribution dependent on the current mode and its duration time, thereby overcoming the limitations of traditional Markov/semi-Markov models in terms of the difficulty in obtaining transition probabilities and computational complexity. Departing from traditional geometric distribution assumption, the proposed method follows more general duration distribution. Acknowledging the challenge of obtaining complete probability information in practical scenarios, partially unknown probability information is considered. In addition, a learning-based asynchronous sliding mode control law is developed, aimed at guiding state signals onto preset sliding regions and effectively reducing chattering induced by mode switchings. Finally, the efficacy and superiority of the developed theories are verified through both numerical and practical examples.

This paper presents a novel event-triggered adaptive generalized super-twisting algorithm (ET-AGSTA) for uncertain nonlinear systems. The remarkable features of the developed method lie in that the control gains can be dynamically adjusted to avoid overestimation, and the transmission of unnecessary signals can be reduced to save computational costs and network resources. Under the proposed ET-AGSTA, it can be proved by the Lyapunov theory that the sliding variable finite-time converges to a desired domain around the origin. The size of the domain can be achieved by modifying the control parameters. Moreover, the lower bound of the triggering time intervals is verified to be always a positive constant, which guarantees Zeno-free behavior in the whole system. Finally, simulation results are given to demonstrate the effectiveness of the proposed scheme.Note to Practitioners— In this paper, the motivation is to handle the problem of the ET-AGSTA design for uncertain nonlinear systems. Although the super-twisting algorithm (STA) is an effective tool for studying uncertain nonlinear systems, there exist two problems in practical applications. On the one hand, the control gains will be overestimated because the upper bounds of the derivatives of the uncertainties are often unknown. This will lead to severe chattering due to the discontinuous term existing in the STA. On the other hand, in actual scenarios, the state signals are periodically sampled and then transmitted to the controllers through the limited network. As the sampling period is usually fixed, it will yield the transmission of redundant signals, resulting in a waste of resources. Therefore, based on the Lyapunov theory, a novel ET-AGSTA is designed to overcome the drawbacks caused by the discontinuous term in the STA. Future work will apply the proposed method to the robotic systems.

This paper is dedicated to a dynamic event-triggeredH∞filtering method of fuzzy Markov jump systems via a mismatched quantization scheme. The system outputs are triggered by a dynamic event-triggered mechanism and then quantized via a mismatched quantizer before being sent to the remote filter. The dynamic triggering scheme with a special diagonal matrix structure threshold is built to reduce the network burden. The quantizer is constructed in a multi-channel paradigm with a time-varying mismatch degree. Then, the remote reduce-order filter is designed to be both fuzzy-rule and mode-dependent. By adopting Finsler's Lemma and the vertex separation method, sufficient conditions are derived in terms of form matrix inequalities. At last, the effectiveness of the proposed method is demonstrated by a tunnel diode circuit.

This paper investigates the event-triggered (ET) fault-tolerant control problem of T-S fuzzy multi-agent systems (MASs) subject to actuator faults. A novel dynamic event-triggering mechanism is proposed to optimize communication transmission efficiency between agents. This mechanism includes some existing event-triggering mechanisms as exceptional cases and allows flexible adjustment of threshold parameters based on the system state variations. Under this triggering mechanism, the fault-tolerant control problem of MASs is transformed into a stability problem of time-delay systems with the help of artificial time-delay methods. Based on this, an artificial-delay-based looped functional is proposed to improve the existing Lyapunov functional. Then, a sufficient condition for fault-tolerance consensus of MASs, which can tolerate larger ET threshold parameters, is derived. Finally, the effectiveness of the proposed ET fault-tolerant control strategy is validated through application to the Duffing-Van der Pol oscillators. Note to Practitioners—The motivation of this paper is to optimize communication resources and deal with potential actuator failures in cooperative control of MASs, which are widely used in autonomous vehicles, industrial automation, and the Internet of Things. It is noted that frequent communication between MASs will cause communication congestion and reduce control efficiency, and actuator failure will inevitably occur due to physical damage or aging. Thus, the study of ET fault-tolerant control not only helps to optimize communication resources, but also ensures the reliable operation of MASs in the face of actuator abnormal situations, guaranteeing stability and reliability in practical applications. This paper proposes a novel dynamic ET fault-tolerant control protocol to achieve cooperative control objectives in the presence of actuator faults. Additionally, an artificial-delay-based looped functional is constructed to enhance the design flexibility of the ET mechanism and controller. The ET fault-tolerant control strategy is applied to the Duffing-Van der Pol oscillator model to verify its effectiveness and practicability.

This paper studies the problem of robustH∞active fault-tolerant tracking control for multi-agent systems (MASs) under nonideal communication links and external disturbances. Unlike most existing studies that assume idealized or simplified communication links, such as uniform constant or time-varying delays, this paper considers a more realistic communication scenario involving non-uniform time-varying communication delays. The communication cases discussed encompass existing common communication scenarios as special cases. To achieve active fault-tolerant control, an intermediate variable-assisted proportional-integral observer (PIO) is introduced to estimate the system’s state and faults. Based on fault estimation values and considering non-ideal communication constraints, a fault-tolerant control scheme is designed to achieve the leader-follower tracking control objectives. With the help of Lyapunov theory and matrix inequality method, sufficient criteria are proposed for co-designing the observer and controller gains. Finally, two simulation examples verify the effectiveness of the theoretical results.

This study addresses the problem of sliding mode control (SMC) for networked non-homogeneous hidden semi-Markov switching systems under semi-Markov kernel and cyber attacks, in which the limited dwell time information is related to the probability density function. Firstly, the model of networked non-homogeneous hidden semi-Markov switching systems under cyber attacks is constructed. Considering that it is difficult to obtain all the real modes in actual systems, a hidden semi-Markov chain is utilised to determine the hidden mode switching of the underlying system, which is more realistic than semi-Markov chain. Based on restricted dwell time probability density function, the common assumption is relaxed under completely known probability density function. The main innovation is to build a suitable SMC scheme under denial-of-service attacks to achieve quasi-sliding mode that eliminates the effect of uncertain parameters. By means of the Lyapunov function depending on the system mode and the elapsed time, stability criteria are considered for the corresponding model. Finally, a DC-DC buck converter circuit model is introduced to validate the practicality of the proposed strategy. Note to Practitioners—Note that the control community has witnessed a tremendous development in networked control systems with wide applications in many practical models, enabling remote control in a sensitive manner through communication networks. Although networked control systems offer obvious advantages, they always suffer from particular difficulties associated with network transmission, such as cyber attacks and packet losses. With the rapidly developing network and communication technologies, networked non-homogeneous hidden semi-Markov switching systems have attracted much attention due to strong capabilities in modeling dynamical systems with abrupt changes in structures or parameters. It is worth emphasising that it is difficult to obtain all real mode information of networked semi-Markov switching systems. This paper offers a novel approach for researchers to investigate the SMC for networked non-homogeneous hidden semi-Markov switching systems under semi-Markov kernel and cyber attacks.

This paper deals with the synchronization for semi-Markovian jump neural networks (S-MJNNs) with DoS attacks and adaptive event-triggered protocol. Aiming at reducing some conservatism of sojourn-time-exponential distribution of Markovian process, the semi-Markovian process is adopted to describe the sudden changes in structures and parameters. As a class of networked systems, the securities of dynamical systems are vulnerable to the DoS attacks that are supposed to obey the frequency constraint. First, on the basis of fully considering the characteristics of aperiodic DoS attacks, a feedback controller is constructed to realize the synchronization among the master-slave systems, in which an adaptive event-triggered protocol is adopted to adjust the amount of triggered data and save network resources more effectively than traditional event-triggered protocol. Then, sufficient conditions to ensure the exponential synchronization and solvable controller gain of the underlying S-MJNNs are given by means of stochastic Lyapunov stability and integral inequality. Finally, the quadruple-tank process model is shown to verify the proposed method.Note to Practitioners—As one of the research hotspots of neural networks, synchronization plays a crucial role in pattern recognition, associative memory, and information science. With the development of computer science and network technology, the information transmission is carried out through the network in the synchronized control of neural networks, which is bound to be affected by the non-ideal network environment. Therefore, the research on synchronization control should not only consider the realization of control method, but also consider the influence of network delay, cyber attacks, and other non-ideal network environment on system performance from the perspective of communication network. In this paper, the synchronization is studied for S-MJNNs with DoS attacks and adaptive event-triggered protocol. On the basis of fully considering the characteristics of aperiodic DoS attacks, the synchronization among the master-slave systems is realized by means of multiple Lyapunov functions and integral inequality. This study provides a new method for the practitioners to study the synchronization control strategy of neural networks affected by the cyber attacks.

This article studies the secure bipartite consensus control issue for a nonlinear multi-agent system (MAS) subjected to hybrid attacks. The challenge is to simultaneously consider improving the bandwidth resource utilization of each agent and ensuring secure bipartite consensus. In response to the former, a component-based weighted try-once-discard (WTOD) protocol is proposed, which can save bandwidth resources by selecting the maximum component of each agent’s measurement output instead of only a single agent’s measurement output to transmit to the observer. Different from the previous work, which considered only one single type/channel attack, a unified hybrid attack model is constructed in which aperiodic denial-of-service (DoS) attacks and unknown false data injection (FDI) attacks occur simultaneously in the channels from sensor-to-observer and controller-to-actuator. At this point, a co-estimation strategy is given to estimate the state of agents and the FDI attack signals. Based on this, the observer-based compensation controller is developed to mitigate the impact of FDI attacks based on the feed-forward method. Meanwhile, a set of sufficient conditions is presented to guarantee the existence of such a resilient controller, which can further ensure the underlying system achieves ultimately bounded bipartite consensus. Finally, a simulation example verifies the feasibility and correctness of the proposed method. Note to Practitioners–Nonlinear MASs currently have numerous real-world applications, such as unmanned aerial vehicle formation control and intelligent transportation. Besides the collaborative behavior among agents, antagonistic interactions are also common in the real world. Thus, the research on the bipartite consensus problem would benefit the application of unmanned missions in complex environments. This paper investigates the secure bipartite consensus control problem for a class of nonlinear MAS under hybrid attacks. Notably, these systems are particularly susceptible to various types of cyberattacks. Therefore, it is crucial to mitigate the impact of cyber attacks on nonlinear MAS that rely on communication links for information transmission. To address the challenges posed by simultaneous DoS and FDI attacks occurring in two communication channels, this paper proposes a resilient compensation-based bipartite consensus control scheme for MASs to proactively mitigate the impact of attacks on system performance. Furthermore, a component-based WTOD protocol is proposed to reduce communication resource usage among agents, making the proposed control strategy easier to implement and more user-friendly for control engineers.

In this paper, we propose a linear quadratic regulator (LQR) for a kind of three-phase two-level voltage source inverter on the basis of grid voltage modulated-direct power control (GVM-DPC) principle. The proposed method has a similar control structure to the conventional GVM-DPC scheme, but the algorithm parameters tuning problem therein could be solved by LQR. Considering the inverter system often converges to a nonzero equilibrium point, an auxiliary variable corresponding to the tracking error is necessary. Unfortunately, when utilizing the LQR technology to solve the optimal tracking problem directly, the dynamic response and steady-state performance might be poor in some situations. By developing a novel auxiliary variable, the drawbacks aforementioned can be avoided and the optimal feedback gain matrix only needs to be calculated once during the control process offline. Then, a Lyapunov stability analysis is used to prove the exponential stability of the system. Finally, simulation and experimental results validate the performance of the proposed method.Note to Practitioners—With the high integration of renewable energy in modern power systems, inverters have become the core device for the transition among electricity, wind, and solar energy. This trend makes the research of power converters regarding control problems increasingly important. Although existing studies can solve some basic problems such as controlling the output current or power of inverters, most of them are based on phase-locked loops, and controller parameters are not easy to tune. This motivates us to develop a user-friendly, low computational burden, easily-to-tune parameters control algorithm, which is convenient to implement on embedded devices to control the output power of inverters. Inspired by this, a direct power control method for inverters using linear quadratic regulator is proposed in this study, and a phase-locked loop-less method is developed by utilizing the theory of grid voltage modulation. Furthermore, this method establishes a new auxiliary variable to ensure global exponential stability of the linear quadratic regulator, resulting in satisfactory transient performance. The above advantages have been further validated through experiments and compared with existing algorithms.

This paper is concerned with the mean-square synchronization issue of the coupled neural networks (CNNs) subject to deception attacks, where the saturation constraint on impulsive control signal is adequately taken into account. To compensate the effect of deception attacks, a hybrid event-triggered mechanism is developed and a minimum inter-event interval is introduced simultaneously to avoid the Zeno phenomena. Under deception attacks, sufficient conditions to guarantee the achievement of the mean-square exponentially synchronization for the CNNs are presented with a novel method combined with mathematical induction, polyhedral representation of saturation nonlinearity, proof by contradiction, which address the actuator saturation in discrete-time control signals effectively. Meanwhile, in consideration of different impulsive effects, three optimization problems are constructed for the sake of acquiring the maximum estimation of the domain of attraction, the feasible maximal impulsive interval and the admissible feasible minimum impulsive interval, respectively. Finally, two numerical simulations are carried out to illustrate the validity of the proposed theoretical analysis. Note to Practitioners—In the engineering and industry, the synchronization control of CNNs exists in many different fields, including image processing, fluid dynamics, and secure communication. The control signals and plant information are transmitted over communication network which is vulnerable to deception attacks. To save communication resources and compensate the effect of deception attacks, a hybrid event-triggered scheme is proposed. Simultaneously, the synthesis of impulse effects and actuator saturation are considered to make results more practical. In addition, three optimization problems are proposed to derive the maximum estimation of the domain of attraction, some related parameters selection of event-triggered scheme.

This paper studies the exponential synchronization issue of complex dynamic networks (CDNs) under dual-channel attacks by utilizing distributed delayed pinning impulsive control strategy. A dual-channel attack model with differentiated node attack probabilities is established by simultaneously considering replacement attacks in the sensor-to-controller channels and injection attacks in the controller-to-actuator channels. Within this approach, The novel concept of average delayed impulsive gains has been introduced to holistically measure the combined temporal influence of impulsive delays and gains across time. Furthermore, by use of the formula for the variation of parameters and applying comparison principle to hybrid delayed impulses, sufficient conditions for achieving the exponential synchronization of the networks are ultimately derived. Finally, numerical simulations verify the effectiveness and superiority of the proposed control strategy.

This paper investigates an intrusion-tolerant control problem for human-in-the-loop multi-agent systems (HILMASs) subjected to external disturbances and unconstrained actuator and sensor false data injection attacks (FDIAs) under directed graph. It is critical to emphasize that once a hacker gets to the control loop of the HILMAS, he can do whatever damage he wants, which means that the attack signal should be free of any constraints. Two main unconstrained attacks, i.e., unbounded FDIAs and variable-frequency FDIAs, are hard to accurately estimate and defend since the widely adopted constraints on the existing FDIAs such as the bounded or/and the bounded first-order derivative have been removed. To tackle this challenging obstacle, a novel unknown-input-proportional-differential observer (UIPDO) is developed to not only reconstruct the follower agents’ states as well as unconstrained actuator and sensor FDIAs simultaneously, but also avoid the decrease of estimation accuracy caused by measurement deviation. It should be noted that this measurement deviation may be extremely large as it is caused by unconstrained sensor FDIAs, which renders the traditional observer ineffective in providing reliable and accurate estimates of the system states and unconstrained actuator and sensor FDIAs. Then, a novel UIPDO-based intrusion-tolerant control strategy without requiring boundedness of the first-order derivatives of the FDIAs as in existing literature is proposed. Furthermore, an adaptive Zeno-free event-triggered mechanism (ETM) solely relying local state information is developed to reduce the communication burden. Finally, the numerical simulation is provided to verify the merits and effectiveness of the developed methodology.

This paper investigates the optimized control problem for nonlinear Markov jump systems with uncertain time-varying parameters. First, the Takagi-Sugeno fuzzy model is applied to describe the uncertain nonlinear Markov jump systems, where the identification of the uncertain time-varying parameters is reformulated as zero-sum game problems. Subsequently, the reinforcement learning-based scheme is employed to solve such zero-sum game problems and achieve optimization even when the system dynamics remain unknown. Afterward, a parallel homotopic scheme is first adopted for such systems. The assumption of an initial stabilizing gain in the policy iteration scheme may be removed in this scheme, naturally enhancing the generality of the developed method. Finally, the actual availability of the proposed parallel homotopic scheme is validated through a single-link robot arm and a series DC motor.

This paper investigates the predefined-time optimized control issue for quadrotor unmanned aerial vehicles (QUAVs) subject to the global prescribed performance constraints and partially unknown states. Under the backstepping framework, a predefined-time global prescribed performance optimized controller is developed through the actor-critic architecture, where the actor executes control actions and the critic evaluates their effectiveness. Furthermore, a neural network-based extended state observer is integrated into the optimized control approach to estimate unknown states and disturbances, enhancing the robustness of the control system, while the performance index is minimized. Notably, unlike most existing prescribed performance control of QUAVs, the requirement for the initial values of prescribed performance functions is removed, thereby reducing conservatism in the optimization of prescribed performance control. Additionally, a second-order command filter is introduced to overcome the “explosion of complexity”. It is demonstrated by the theoretical analysis that all closed-loop signals are bounded, while the position and attitude tracking errors subject to the global prescribed performance constraints converge into a neighborhood near zero within a predefined time. Finally, the feasibility and effectiveness of the proposed control strategy are validated through a simulation example.

This paper presents an observer-based fuzzy event-triggered load frequency control (LFC) strategy and a neural network (NN) approximation approach for wind power systems (WPSs) facing network resource constraints and multiple cyberattacks. First, a Takagi-Sugeno (T-S) fuzzy model is developed to capture the nonlinear dynamics of the wind turbine. A novel multiple cyberattack model is then proposed, relaxing stringent assumptions about denial-of-service (DoS) and false data injection (FDI) attacks prevalent in prior studies. An observer-based dynamic event-triggered controller is subsequently designed to reduce network burden in WPSs while mitigating DoS effects. This controller incorporates an NN-based online weight adjustment algorithm to approximate and counteract the false data injected by FDI attacks. Lyapunov analysis demonstrates that the augmented error system achieves asymptotic stability with H∞performance in the absence of DoS attacks and maintains uniformly ultimate boundedness under multiple cyberattacks. Finally, a numerical example validates the effectiveness of the proposed control strategy and NN approximation approach.

This paper studies the outlier-removal memory-event-triggered proportional-integral state estimation for variable-speed wind turbine systems subject to multimodal deception attacks. An innovative outlier-removal memory-event-triggered mechanism using historic data and comparison error upper bound is proposed to enhance the utilization of the limited network resource. The first merit of this mechanism is that the average value of some historical data is employed to smooth the measured outputs with stochastic fluctuations, which is able to reduce the unnecessary triggering events caused by the fluctuations. The second one is that the triggering error term’s upper bound is introduced to avoid the false transmissions induced by measurement outliers. A multimodal deception attack model with different occurrence probabilities for different modes is considered, which is more general and practical than the existing single-modal model. In order to estimate the system state, a fuzzy proportional-integral estimator is presented. With the introduction of an extra integral term, the presented estimator has the potential to improve the estimation performance compared to the traditional proportional estimator without the integral information. Then, the closed-loop estimating error system for variable-speed wind turbine is established as a T-S fuzzy system. Resorting to the Lyapunov theory and linear matrix inequality technique, a set of sufficient conditions for co-solving the estimator gains and triggering matrices are produced. Ultimately, the effectiveness of the developed method is verified by some simulation results.

This article considers the observer-based containment control problem of multiagent systems (MASs), with a focus on the scenario where the control task only utilizes intermittent communication among agents and from the sensor to the local state observer. To deal with this problem, we propose a hybrid triggering control strategy, which includes two parts. First, a local observer for each follower is proposed to estimate state under a time-triggered mechanism (TTM). Second, based on local observer, a fully distributed containment controller is proposed under a node-based hybrid triggering mechanism (HTM), which does not require global information. Note that such HTM includes a TTM and a dynamic event-triggered mechanism (DETM). The most significant merit of this article is that the TTM is developed to provide the guarantee of the positive minimum triggering interval time (MTIT) between two triggering actions, which is more desirable than simply excluding Zeno behavior in practical applications. Finally, a simulation example on containment of nonholonomic mobile robots illustrates the control strategy.

Modern power systems maintain frequency stability through load frequency control (LFC), which is affected by delays of control signals and the volatility of renewable energy sources. This paper investigates the robust problem of LFC for wind power systems with multi-source-induced delays, considering the significant variations in delay magnitudes across different time intervals due to diverse inducing sources. Firstly, the LFC is modeled as a closed-loop system with two switching modes by dividing the delay into two parts with different magnitudes based on the inducing sources. Then, the controller design criteria for a multi-source-induced delay-dependent LFC system are proposed through the switching system theory. Finally, case studies conducted on two-area and three-area LFC system validate that the method proposed in this paper can enhance the robust performance of the system compared to general methods, and it is also capable of handling certain multi-source-induced delay phenomena that are beyond the capabilities of general methods during the operation of LFC systems. Note to Practitioners—Frequency stability in multi-area power systems is facing growing challenges due to delays caused by various sources. Existing methods for delayed power systems often overlook these diverse delay sources, treating them as uniform, which may reduce system robustness. This paper proposes a robust load frequency control (LFC) scheme based on a delay-induced-source dependent approach, which aims to ensure the exponential stability of multi-area LFC systems. By modeling multi-source-induced delays using switching modes and designing controllers through switching system theory, this method effectively addresses the complexity of multi-source-induced delays. Furthermore, it enables the design of controllers tailored to the type of delay-inducing source, enhancing system stability under different delay types. Practitioners can apply this method to enhance the reliability of LFC systems in power systems experiencing diverse delay scenarios. It is especially effective in situations where existing methods struggle to handle complex delays, providing a practical solution for maintaining frequency stability in modern networked power systems.

Notwithstanding the successful design of state-of-the-art cooperative control protocols to accomplish formation tracking for multiple unmanned aerial vehicles (UAVs), the assurance of performance optimality cannot be guaranteed in the face of complex disturbances affecting these multi-UAV systems. In order to surmount this challenge, this research endeavor aims to establish a feedforward-feedback learning-based optimal control methodology to facilitate cooperative UAV formation tracking in the presence of intricate disturbances. To be more precise, by leveraging backstepping-based feedback control, the problem of UAV formation tracking is transformed into an equivalent optimal regulation problem. Consequently, a learning-based feedforward control scheme is devised, wherein the cooperative policy iteration algorithm is formulated based on a two-player zero-sum game. The critic-only echo state network (ESN) is employed to approximate the optimal feedforward control policies, with the inclusion of an online adaptive tuning law and compensation terms to alleviate the persistence of excitation condition and eliminate the need for an initial admissible control. As a result, the closed-loop stability is guaranteed in terms of uniformly ultimately boundedness for tracking errors and ESN weights.Note to Practitioners—In real-world scenarios, the flight of multiple UAVs is invariably affected by intricate disturbances, resulting in compromised tracking precision. There is an urgent need to enhance resistance to disturbances and ensure optimal performance for cooperative formation tracking of multiple UAVs. Beyond the capabilities of model-based controllers, the integration of reinforcement learning has shown promise in achieving robust control actions. By introducing the cooperative policy iteration algorithm based on a two-player zero-sum game, the tracking performances of UAV formation can be further optimized. In order to facilitate the practical application of reinforcement learning in UAV systems, our proposed algorithm addresses the persistency of excitation condition by incorporating innovative compensation terms into the ESN tuning law. Furthermore, we resolve the requirement for initial admissible control by introducing a novel piecewise compensation term into the ESN tuning law, which is based on a newly proposed Lyapunov function.

This paper investigates the dynamic event-triggered boundary control of cyber-physical flexible riser systems under spoofing attacks. It aims to dampen vibrations resulting from riser pipe deformation, alleviate the communication load, and curtail the influence of such attacks. Firstly, a novel dynamic event-triggered boundary controller is constructed by utilizing external disturbances and event-triggering strategies. This controller overcomes the complex spatio-temporal coupling phenomena in riser systems and suppresses vibrations. Secondly, a controller is developed to minimize the impact of false data injection on the cyber-physical flexible riser system due to uncertain spoofing attacks. Finally, the stability of the system is mathematically analyzed to prevent the occurrence of Zeno phenomena. The simulation results further validate the effectiveness of the proposed method. Note to Practitioners—Cyber-physical flexible risers play an important role in various marine engineering and industrial fields, such as underwater oil and gas extraction, deep-sea resource exploitation, and seabed exploration. A challenge within these domains is the effective mitigation of cyber-physical riser vibrations amidst spoofing attacks, a problem that has yet to be resolved. This paper introduces a novel dynamic event-triggered boundary control strategy aimed at dampening vibrations induced by riser deformations, reducing communication overhead, and attenuating the effects of spoofing attacks. Based on PDEs, the proposed control strategy offers a solution for the regulation of flexible riser systems in forthcoming engineering endeavors.

This paper predominantly studies how to achieve the mean-square exponential stability (MSES) of stochastic highly nonlinear fuzzy systems with Markov jump and periodic time-varying delay (SHNFSMPTD), using asynchronous switching fuzzy control. Considering the time-varying delay and the drift and diffusion terms satisfying polynomial growth conditions, we construct two different functionals that explicitly depend on time t in the monotonically increasing and decreasing intervals, respectively. Combined with Dupire’s functional Itô formula, graph theory, and the properties of periodic time-varying delay, we can show that the negative definiteness of the functionals holds in both intervals, which leads to the stability of systems. In addition, by combining the generator matrix of Markov jump and the conditional probability matrix of asynchronous switching, we provide additional MSES criteria for SHNFSMPTD under asynchronous switching fuzzy control. Ultimately, the theoretical results obtained are utilized in a class of van der Pol-Duffing oscillators (VDPDO), and the six-robot systems with center pattern generator networks, and the feasibility of the theoretical results is illustrated through numerical simulations. Note to Practitioners—Stochastic highly nonlinear fuzzy systems with Markov jump and periodic time-varying delays are widely found in many practical systems, such as communication networks, control systems, and biological rhythm systems. In this paper, we propose for the first time to deal with the exponential stability problem of such systems using Dupire’s functional Itô formula. By constructing suitable functionals and applying Dupire’s functional Itô formula, we obtain the negative definiteness of the operator in both intervals. This paper provides a new method for the construction of Lyapunov functional and offers some new ideas for the stability study of this kind of systems. In the future, we attempt to study the noise stabilization of such systems with various attacks and intermittent communication under intermittent control.

This paper investigates the stabilization of highly nonlinear stochastic delayed large-scale networks (HSDLN) based on asynchronously delayed intermittent decentralized control (ADIDC). By incorporating highly nonlinearity into the networks, practical problems can be more accurately modeled, and the nonlinear coupling relationships among nodes can be effectively captured, vastly expanding the practical application scope. ADIDC effectively integrates the advantages of asynchronously intermittent decentralized control and delayed intermittent control, enabling the networks to achieve the desired behavior. Subsequently, we set up an auxiliary timer and then define a newfangled Lyapunov functional. In particular, an innovative Lyapunov-based analysis, not utilizing Halanay-type differential inequality, is introduced to obtain a stabilization criterion for HSDLN, which effectively copes with asynchronously intermittent decentralized control and highly nonlinear, stochastic, and delayed elements. Meanwhile, a graph-theoretic methodology is employed to address the cross-terms arising in an asynchronous sense. Ultimately, as a practical application of the theoretical findings, a class of FitzHugh-Nagumo models is considered, and the stabilization is validated through numerical simulations. Note to Practitioners—This paper is motivated by existing results on various intermittent controls for the dynamics of complex networks. The existing results mainly require intermittent controls to be continuously/discretely synchronous sampled-data each node of complex networks, which limits the application scope of intermittent controls. This paper designs a new hybrid control called asynchronously delayed intermittent decentralized control. Particularly, asynchronously intermittent sampled-data control is a special case of control in this paper. Moreover, some lower conservative sufficient conditions are derived, such as local Lipschitz condition and polynomial growth condition instead of global Lipschitz condition and linear growth condition for coefficients of complex networks, and the average work time ratio instead of the maximum rest time ratio for intermittent control. The proposed results provide a novel idea for the stabilization study of FitzHugh-Nagumo models, and it is expected that the proposed methods can be extended to more actual physical engineering systems.

This paper considers intelligent event-triggered${H}_{\infty }$Load Frequency Control (LFC) for a new kind of multi-area power system. Firstly, delay factors are introduced into the prime mover, engine, and energy storage unit, i.e, multiple-resource delays, and the impact of these delays on the dynamic behavior of system is discussed for the first time, making the model more realistic and accurate. Secondly, a new lemma is proposed, which introduces free variables, extending to the scenarios with time delays. Combining this lemma with the event-triggered mechanism, the control scheme is designed, which is featured by two characteristics. On the one hand, in the Lyapunov functional, looped terms on neighbor triggering instants are considered, reduce the conservatism of the obtained criteria by capturing the information of triggered instants. Meanwhile, a mandatory triggering mechanism is introduced to break the possible dead loop caused by long-term no-triggering. On the other hand, a genetic algorithm (GA) is employed to optimize the parameters of the trigger threshold, encouraging efficient control within the context of LFC and delays. The effectiveness of the proposed approach is confirmed by a typical numerical example with four case studies.

This study investigates an asynchronous sliding-mode control (SMC) strategy tailored for interval type-2 (IT2) fuzzy switching systems, specifically addressing challenges posed by cyber-attacks. Distinct from existing stochastic switching strategies, a novel duration-time-based switching rule is proposed that integrates both sojourn probability and mode duration, significantly reducing computational complexity and aligning more closely with practical requirements. To mitigate mode-switching-induced chattering and enhance robustness against uncertainties and disturbances, an innovative fuzzy SMC law with a learning mechanism is developed. Notably, a recursive sliding-mode learning controller is introduced, replacing abrupt switching actions with iterative learning adjustments to progressively guide system states onto the sliding surface, thereby significantly improving control smoothness and reducing chattering. To effectively handle cyber-attacks disrupting mode transmission, a comprehensive mismatched model that dynamically synchronizes the modes of the system and the controller is introduced, offering improved resilience compared to traditional fixed mismatch approaches. Utilizing the proposed learning-based SMC and Lyapunov stability theory, sufficient conditions ensuring mean-square stability of the system are derived. Finally, the practical effectiveness and distinct superiority of the proposed methods are demonstrated through simulations using mass-spring-damper and tunnel diode circuit models.

This article delves into the semi-global robust output regulation of uncertain singular linear systems subject to input saturation and denial-of-services (DoSs) attacks. In the event of a DoS attack, the communication channel between the plant and the controller is blocked. To address the input saturation nonlinearity and to enhance robustness against uncertainty and Dos attacks, a post-processing internal model-based robust output feedback regulator is proposed. It employs the small gain techniques, incorporating two small positive parameters. The overall system is modeled as a hybrid system using hybrid formalism, for which a jump corresponds to the DoS attacks. It is shown that the proposed regulator exhibits robustness against certain DoS attacks and structure uncertainties. Consequently, the output error can asymptotically converge to the origin when the structured uncertainty is small enough, and the proposed constraint on the duration of DoS attacks is met. Finally, two numerical example are given to illustrate the effectiveness of the result.

This article considers the design of prescribed-time sensor fault estimators (PSFEs) and unknown input compensation for linear systems, i.e., estimators that estimate the sensor fault and controllers that stabilize the state of the system both at a prescribed time. With this intention, a filter is introduced to eliminate the limitation of the fault being differentiable. Then, the task of designing PSFEs can be converted into the task of designing prescribed-time unknown input observers for the augmented systems. Next, the generalized inverse is employed to convert the augmented systems into a form suitable for observer design. Then, the PSFEs are developed by exploiting periodic delayed output. Both full- and reduced-order PSFEs are taken into account. In addition, based on the established state observer and unknown input estimation, periodic delayed controllers are designed to completely compensate for the unknown input so that the closed system is T-prescribed-time stable (T-PS). Finally, an example is provided to demonstrate the efficacy of the proposed methods.

In this work, membership-functions-dependent resilient hybrid-triggered (HT) passivity control is considered for networked fuzzy systems against deception attacks. To mitigate the transmission burden and enhance resilience to attacks, a resilient membership-functions-dependent HT mechanism is proposed that considers the properties of a Takagi-Sugeno fuzzy model. The presented HT strategy incorporates membership functions to generalize the HT threshold and weighting matrices. Then, the membership functions are embedded in quadratic and integral terms of the fuzzy Lyapunov-Krasovskii functional, leading to a more effective resilient HT mechanism that significantly reduces network bandwidth stresses and achieves better passivity performance. An asynchronous passivity fuzzy controller with enhanced feasibility is designed under imperfect premise matching, and traditional passive controllers are generalized as special cases. Finally, a truck-trailer example is offered to test the validity and applicability of the proposed techniques.

This article considers the issue of adaptive fixed-time tracking control for a class of stochastic high-order nonlinear systems (HONSs) with full state constraints. Unlike the existing results, a Barrier Lyapunov function (BLF) with fractional form is employed to deal with asymmetric full-state constraints. Fuzzy logic systems are employed to resolve stochastic disturbances and unknown nonlinearities. Based on adding a power integrator technique and backstepping method, an adaptive fixed-time fuzzy state feedback controller is proposed to ensure that all signals are bounded and all states are always within the constrained interval. The nonlinear system is fixed-time bounded in probability by the fixed-time Lyapunov stability theory. Without altering the controller structure, the BLF can be used in unconstrained high-order systems. Two simulation experiments including a spring-mass damper system prove the effectiveness of the designed control strategy.

A dynamic event-triggered load frequency control (LFC) is studied for interconnected multiarea power systems (IMAPSs) with stochastic semi-Markov parameters from the perspective of finite-time interval. To facilitate the sudden changes, the underlying semi-Markov process (SMP) is adopted to characterize the random behavior of IMAPSs. A dynamic event-triggered protocol (DETP) is developed to modulate the transmission frequency while maintaining predefined system performance. Owing to complicated grid environment, the hidden semi-Markov model (HSMM) is proposed to solve the asynchronization between the system mode and the controller mode, which forms a new asynchronous mechanism to better understand the behavior pattern of the system. The novelty of this article is to construct a suitable asynchronous control strategy to solve the mismatch between the system mode and the controller mode under the framework of IMAPSs. Different from static event-triggered protocol (ETP), the DETP is proposed, in which the threshold parameters can be dynamically adjusted to reduce the waste of communication resources and achieve dynamic performance in limited time. According to the stochastic system theory and the finite-time theory, by constructing a modular dependent random Lyapunov function, sufficient conditions are obtained to ensure the finite-time boundedness of the corresponding system with performance. Finally, the efficiency is demonstrated through three-area power systems.

This article is concerned with the observer-dependent asynchronous control problem in cyber-physical systems (CPSs) vulnerable to multimode attacks, with a specific focus on addressing the significant challenge posed by the surreptitious nature of attack behaviors. First, in view of band-limited communication channels in CPSs, the quantizer is used to quantize the measured output. Second, owing to the open and shared network of CPSs, the data transmission process is more susceptible to attacks. A multichannel transmission framework is constructed under the assumption that each transmitted data element is susceptible to potential attacks. The switching dynamics among various attack forms launched by the adversary on the transmission channel are governed by a semi-Markov chain. The analysis of multimode attacks with stealth characteristics is conducted within the framework of a hidden semi-Markov jump mode, which is achieved by establishing a dual-layer stochastic process comprising a multimode sequence and an observed mode sequence. Leveraging emission probability, we design an observed-mode-dependent controller capable of stabilizing the system even in the absence of direct access to the actual attack patterns and in the presence of information loss. The simulations involving a single-channel unmanned ground vehicle system and a mass-spring-damper system with two channels are provided to validate the feasibility and efficacy of our proposed methodology.

This article studies the secure leader-follower consensus of nonlinear multiagent systems under multimode Denial-of-Service attacks. According to the topology characteristics after attacks, three kinds of different attack modes are introduced. Moreover, the common zero-topology attack is encompassed within the multimode attacks considered in this article. Due to the difficulty of directly obtaining the state of the system in some circumstances, a state observer is designed utilizing localized output information of the MASs to estimate the state. By applying suitable observer-based controller and Lyapunov method, some sufficient conditions are presented to ensure the observer-based secure consensus of the MASs under multimode DoS attacks. Furthermore, the obtained results can be extended to linear MASs under DoS attacks. Finally, two practical examples on power system are provided to demonstrate the validity of the derived theoretical results.

This study presents a reinforcement learning (RL)-based adaptive fuzzy event-triggered optimized tracking control strategy for slowly switched nonlinear systems with stochastic disturbances in the prescribed set-time performance. The designed optimized event-triggered mechanism for the subsystems effectively solves the asynchronous switching problem with no limit on the maximum asynchronous time. Moreover, the tracking performance of system can be optimized significantly using an RL strategy. Using the lemma proposed in the study (Lemma 3) and the normalized function, it is shown that under the performance constraint approach, the selection of the performance function is consistent with the control protocols. By adopting the modified admissible edge-dependent average dwell time method and the optimal controller, the boundedness of closed-loop system signals is proved, and the Zeno phenomenon does not occur. Finally, the superiority of the optimized strategy is verified using numerical simulations and a practical single-link manipulator.

To achieve low-carbon operation of a distribution network, new energy resources like photovoltaics (PVs) have been extensively integrated into it. However, this integration poses significant challenges to the supply-demand balance. Specifically, the generation of PVs is stochastic, causing power fluctuations. Additionally, the increase in power data and the open nature of the network will cause network congestion and communication disturbances. To address these issues, an active power support (APS) strategy is developed with the following innovations. First, an adaptive mutation-based generation prediction algorithm incorporating a multi-extreme learning mechanism (ELM) is proposed to optimize the prediction model and provide reliable predicted generation data for regulation. Second, a demand-driven path optimization method is proposed to prioritize critical data transmission, ensuring that regulatory service demands are met while mitigating congestion. Third, a hierarchical control strategy utilizing multifactor matching and a sliding mode controller (SMC)-based virtual leader-following consensus algorithm is designed to generate optimal control commands for PVs and suppress disturbances. Finally, adequate simulations demonstrate that the proposed method reduces the prediction error by at least 10.1% compared to existing methods, adjusts transmission paths based on data importance and service needs to mitigate congestion, and suppresses communication disturbances within 1s, thereby enabling effective APS.

This article investigates observer-based optimal fuzzy control for networked interval type-2 (IT-2) fuzzy systems against hybrid cyber-attacks, including aperiodic denial-of-service and deception attacks. First, a hybrid cyber-attack model is constructed after denoting a nonlinear system with parameter uncertainties and external disturbances via an IT-2 fuzzy model. Then, the switched fuzzy observer is derived to estimate the unmeasured states of the system. Second, by introducing the newly designed algorithm, a novel optimal adaptive fuzzy event-triggered strategy is proposed. This mechanism effectively reduces the communication burden and enhances flexibility in handling hybrid cyber-attacks. Third, by employing a new piecewise Lyapunov–Krasovskii functional that incorporates membership functions (MFs), a fuzzy controller with imperfect MFs is designed, where both the MFs and their derivatives are considered. Eventually, sufficient conditions are developed to ensure that the networked IT-2 system is mean square exponentially stable with$ H_\infty$performance. Finally, the merits and applicability of the proposed approaches are exemplified by a practical example.

The aim of this study is to address the challenges presented by the nonstrict feedback (NSF) stochastic nonlinear systems structure with delayed state and delayed input. The Pade approximation and an intermediate variable are used to handle the input delay, transforming the original system into one that is perturbed only by the state delay. Subsequently, a novel Lyapunov–Krasovskii function (LKF) related to all states is proposed to effectively compensate for the delay term. In addition, fuzzy logic systems (FLSs) and the variable separation concept are employed to manage the complexity of the system structure. Based on the fixed-time Lyapunov criterion, the closed-loop system bounded in probability is guaranteed within a fixed time, and all signals in the system are bounded. Finally, two stochastic nonlinear systems are utilized to demonstrate the effectiveness of the proposed control strategy.

This article investigates the secure leader–following consensus of multiagent systems with unknown follower dynamics under two types of denial-of-service attacks: connectivity-maintained and connectivity-broken attacks. A predefined-time resilient distributed observer to achieve predefined-time observation of the leader's state is constructed by incorporating a time-varying piecewise function with an online update at the initial instant. During the connectivity-maintained intervals, all agents can achieve predefined-time observation of the leader. During the connectivity-broken intervals, agents that have direct or indirect access to the leader's state can still accomplish the predefined-time observation. Since the leader state observation cannot be ensured for all agents during the connectivity-broken intervals, a new persistent dwell-time switching model is introduced to describe the switching between the two types of attacks, and novel sufficient conditions are derived to ensure that all agents can observe the leader's state based on multiple Lyapunov function approach. Furthermore, a predefined-time sliding mode controller combining the resilient distributed observer and fuzzy reinforcement learning strategy is proposed to ensure leader–following consensus. Finally, the effectiveness of the proposed method is validated through simulations and comparisons using a distributed multiple robot arm system.

This article investigates the problem of privacy-preserving average consensus for continuous-time heterogeneous multiagent systems with intermittent information transfer under asynchronous sampled-data interactions. To address the challenges posed by agent-specific asynchronous sampled-data and time-varying communication delays, a time-translation approach incorporating a shared sampling period strategy is introduced, effectively transforming the asynchronous problem into a synchronous framework. Next, integrated distributed hybrid controller with time-varying noise injection is designed, enabling agents to interact with sensitive information only at sampling instants, thereby preserving privacy while maintaining trajectory availability. Then, the time-varying step-size and noise parameters, which are tunable parameters of the dual control mechanism corresponding to the desired ε-differential privacy budget and system convergence accuracy are proposed, and the trade-off between control performance and privacy preservation is thoroughly analyzed. It is shown that the proposed protocol achieves asymptotically unbiased mean-square output consensus with predefined accuracy and privacy budget. Numerical examples validate the theoretical results.

In this paper, an exquisite strategic denial-of-service (DoS) attack mechanism is proposed, whose objective is to intercept specific measurements obeying the Round-Robin protocol (RRP) and enlarge the attack destructiveness at the side of the remote estimator. The distinctive contributions of this research can be stressed as follows: firstly, in contrast to the majority of existing DoS attack policies, the property of the considered attack sequence is stochastic, selectively targeting information crucial to the transmission process. Secondly, the concerned attack mechanism yields great disruption compared to the periodic or Bernoulli-distributed DoS attack models. Additionally, the joint consideration of the limited average attack rate (AAR) and energy allocation procedure (EAP) reflecting energy constraints of DoS attacks, are merged into a unified random framework. The relationship between these constraints and attack probability is established by resorting to stochastic analysis methods. Finally, two numerical examples are provided to demonstrate the effectiveness and usefulness of the proposed attack policy.

This article is devoted to dealing with the secure synchronization for complex networks suffering from deception attacks via impulsive control. Notably, false data would be injected in a fully distributed manner with various attack signals diffusing in node-to-node communication channels. The controlled error system is implicitly represented in the form of tracking errors within the error conjunction matrix. In consideration of the risk that synchronizing impulses may be transformed into impulsive disturbances due to malicious attacks, an extended definition of average impulsive gain is introduced to describe the hybrid impulse sequence containing diametrically opposed functions. Moreover, a self-triggered mechanism integrating memory characteristic is built to regulate impulse activations. By leveraging historical temporal data, the issue of active attacks induced by activation impulse signals is effectively circumvented. With the Lyapunov stability theorem, the error conjunction matrix, and the comparison system-based method, sufficient conditions for secure synchronization could be obtained. The given simulation examples show that the self-triggered impulsive controller could perform well under the attack scenarios.

This article studies the event-triggered formation control of multiple unmanned aerial vehicles (UAVs) in the presence of deception attacks. Unlike existing research focusing on deception attacks, the secure upper bound of deception attacks that the formation tracking of UAVs can tolerate is estimated to reduce the conservatism associated with the predefined upper bound of deception attacks. A dynamic event-triggered mechanism is developed by considering triggering and tracking errors to reduce data release rates while maintaining desired formation tracking performance. Leveraging information from neighboring UAVs, tracking control strategies for the multi-UAV system facing deception attacks are designed using the Lyapunov stability theory. Simulation analysis validates the effectiveness of the proposed strategies, demonstrating improved resilience in the presence of deception attacks.

This article focuses on the distributed optimization problem of heterogeneous nonlinear multiagent systems with coupling constraints. By decoupling the design, the problem is transformed into two components: the optimal signal estimator design with coupling constraints and the tracking controller design with nonlinearity. To achieve this, a novel optimal signal estimator is proposed, which effectively manages coupling constraints without requiring global information. The estimator incorporates adaptive gains, a crucial element for achieving a fully distribution algorithm. In addition, a correction term is introduced in the adaptive gain update law to filter out high-frequency signals and enhance the algorithm’s robustness. To further reduce communication frequency, a stochastic event-triggered mechanism is employed, allowing agents to trigger with a certain probability. Using the optimal signal and its higher order derivative trajectories generated by the estimator as reference signals, a tracking controller is constructed through backstepping adaptive technique to ensure asymptotic tracking and effectively solve the distributed optimization problem. Finally, the effectiveness of the optimization algorithm is validated through simulation.

This article is focused on the secure consensus of multi-agent systems (MASs) with Euler-Lagrange (EL) dynamics and multiple DoS attacks. First, a new model of the multiple DoS attacks is built, which is based on discrete sampled-data communication and considers the joint impact of the multiple DoS attacks. Second, under multiple DoS attacks, two new technical results are proposed, which lay a good foundation for consensus analysis. Then, by designing an adaptive distributed control (DC) protocol combining with two new auxiliary systems, secure consensus results are derived for MASs with EL dynamics and multiple DoS attacks. Finally, simulations based on networked two-linked robot manipulators are presented to show the effectiveness of the theoretical results.

This article presents a suboptimal joint trajectory replanning (SJTR) method for Mars ascent vehicle (MAV) launch missions under propulsion system faults. Conventional step-by-step trajectory replanning may fail to make timely decisions, risking mission failure. The SJTR method formulates a joint convex optimization problem of target orbit and flight trajectory after a fault. By applying penalty coefficients for terminal constraints, it adheres to the orbit redecision principles, enabling a concise and rapid solution. To further enhance the convergence and the accuracy of orbit-type determination, a learning-based warm-start scheme is proposed. Offline, a deep neural network (DNN) is trained with data generated by various trajectory replanning methods following the redecision principles. Online, the DNN provides initial guesses for the time optimization variables based on the fault scenario. Numerical simulations on mass flow rate and specific impulse drops validate the reliability of the proposed method, demonstrating at least 49.5% higher computational efficiency compared with the upgrading and downgrading replanning methods.

This article addresses the finite-time neural predefined performance control (PPC) issue for state-constrained nonlinear systems (NSs) with exogenous disturbances. By integrating the predefined-time performance function (PTPF) and the conventional barrier Lyapunov function (BLF), a new set of time-varying BLFs is designed to constrain the error variables. This establishes conditions for satisfying full-state constraints while ensuring that the tracking error meets the predefined performance indicators (PPIs) within a predefined time. Additionally, the incorporation of the nonlinear disturbance observer technique (NDOT) in the control design significantly enhances the ability of the system to reject disturbances and improves overall robustness. Leveraging recursive design based on dynamic surface control (DSC), a finite-time neural adaptive PPC strategy is devised to ensure that the closed-loop system is semi-globally practically finite-time stable (SPFS) and achieves the desired PPIs. Finally, the simulation results of two practical examples validate the efficacy and viability of the proposed approach.

In this article, the problem of data-driven control for Markovian jump systems (MJSs) under aperiodic sampled-data (ASD) control is studied. By constructing a loop-based Lyapunov functional (LBLF), the stability criterion of MJSs based on the model is established. Combining this model-based condition with data-based representations, two distinct stability conditions are formulated. ASD controllers are subsequently designed based on these conditions. The proposed approach is validated by modeling an aircraft system as an MJS, with flight test data showing that the controller effectively stabilizes the system, whether the system matrices are known or unknown. Furthermore, a numerical example highlights that the proposed approach achieves a larger upper bound for the sampled-data period.

This article focuses on the dynamic quantized control for Takagi–Sugeno fuzzy semi-Markov jump systems (T–S FSMJSs) under fading channels and deception attacks, employing an improved event-triggered mechanism (ETM) strategy. Specifically, a more generalized semi-Markov process (SMP) that is governed by a higher-level deterministic switching signal (HLDSS) is addressed. A novel dynamic ETM is skillfully developed based on the information of quantization and two internal dynamic adjusting variables to further enhance the network bandwidth utilization. The dual asynchronous phenomenon between the plant and controller (asynchronous modes and mismatched premise variables) is addressed. This implies that the designed controller is not required to share the same membership functions and modes as the plant, establishing a more rational structure. The hidden semi-Markov model (HSMM) is attained to characterize the stochastic varying channel fading amplitudes. The Lyapunov function incorporating mode and fuzzy information is constructed to establish sufficient criteria ensuring the strictly dissipative performance and mean-square exponential stability (MSES) of the resulting closed-loop systems, and a new security fuzzy dual asynchronous controller is then developed. Finally, the efficacy and applicability of the results are demonstrated through numerical and practical examples.

This article investigates adaptive output feedback fuzzy control for networked nonlinear systems with hybrid attacks using fuzzy information learning approach. First, the systems with external disturbances and parameter uncertainties are modeled via a fuzzy framework, and an improved hybrid attack model is developed by incorporating aperiodic DoS and FDI attacks. The adaptive event-triggered (ET) output feedback controller is proposed to handle unmeasured states and resource constraints. Then, to capture fuzzy information, a piecewise fuzzy Lyapunov–Krasovskii functional incorporating membership functions (MFs) is meticulously constructed using delayed fuzzy system theory. The fuzzy information learning algorithm is further designed to determine and exploit the actual lower and upper bounds of MF’ derivatives, resulting in an effective controller with enhanced control performance and resource efficiency. Eventually, the ET strategy parameters, controller gains, and the optimal performance index are derived within a unified framework, ensuring global exponential stability and guaranteedH∞performance. Finally, the merits and applicability of the proposed approach are exemplified by a practical example.

A novel state estimation and fault detection (FD) method with a three-stage zonotopic Kalman filter (TS-ZKF)-basedH∞augmented state observer is proposed for a time-delay system with an actuator fault. First, the time-delay system is augmented to decouple the state delay and actuator fault. Second, an observer is designed for the augmentation system by using theH∞performance index, and the interval of state estimation is obtained. Then, the size of the zonotope is minimized to further reduce the state estimation residual and to narrow the interval. The actuator fault estimation and its bounds of the original system are extracted from the augmented state estimate vector. Finally, a numerical simulation and a case on the buck–boost circuit both verify the conservatism and accuracy of the proposed algorithm.

In this study, a novel self-learning control is proposed for nonlinear quarter-car suspension system (QCSS) based on Markov jump model with completely unknown dynamics to solve the optimal control issue. The interval type-2 (IT2) fuzzy method is used to overcome the uncertainty problem and the zero-sum game approach is adopted to transform the optimal control issue, so as to achieve Nash-equilibrium. Based on the framework of reinforcement learning (RL), an off-line policy iterative algorithm is proposed to solve the fuzzy random coupled algebraic Riccati equation (ARE). Due to the complexity and variability of the actual system, it is difficult to obtain the complete dynamic information. A self-learning algorithm is designed based on the off-line algorithm, which does not require any dynamic information of the system, relying on the system state and control input for iteration. Furthermore, the Lyapunov stability theory is adopted to ensure that the system is asymptotically stable withH∞performance index. Finally, a simulation example is given to explain the effectiveness of the control method.

This article considers the saturated distributed impulsive local exponential synchronization (LES) issue for directed coupled neural networks (CNNs) with proportional delay and distributed delay under the impulsive time window (ITW) scheme. By utilizing a modified compact convex hull representation of the saturation nonlinearity, the extended parameter variation formula, and the proportional delayed impulsive comparison principle, sufficient conditions for the local exponential synchronization (LES) of the CNNs subject to actuator saturation are obtained within the domain of attraction (DOA). Based on these conditions, an optimization problem constructed by transformed linear matrix inequality (LMI) constraints is formulated to determine the impulsive control gain for enlarging the estimation of DOA as much as possible with a predetermined exponential convergence rate. Ultimately, a numerical simulation is exhibited to illustrate the feasibility and validity of the theoretical results.

This article investigates the leader–follower consensus tracking problem for Lipschitz nonlinear multiagent systems (MASs) with unmeasurable states. To address limitations in communication and sensing resources, a hybrid triggering consensus framework is proposed to reduce redundant transmissions and measurements. First, a local observer is designed to estimate the system state, where output sampling and transmission to the observer are determined by a time-triggered mechanism (TTM). Then, based on the observer and two nonlinear estimators, a fully distributed controller is developed. Communication among neighboring agents is governed by a hybrid triggering mechanism (HTM) that integrates both time-triggered and event-triggered conditions. Notably, the proposed strategy ensures a positive minimum triggering interval (PMTI). The effectiveness of the proposed method is validated on a single-link flexible joint manipulator.

This article investigates the issue of asynchronous event-triggered secure control for Markov jump systems (MJSs) under aperiodic denial-of-service (DoS) attacks. A memory-based mode-dependent resilient event-triggering scheme (MMRETS) based on aperiodically sampled data is designed to avoid Zeno behavior and continuous monitor, which has the advantages in mitigating the transmission times and tolerating the DoS attacks. Meanwhile, the asynchronous circumstance between the system and corresponding event-triggered controller in the absence of DoS attacks is characterized via a hidden Markov model (HMM). Subsequently, by using a novel Lyapunov functional and an iterative approach, sufficient conditions are obtained to guarantee the${\mathcal {H}}_{\infty }$performance of the resultant Markov closed-loop system. Finally, a practical truck-trailer model example is provided to illustrate the efficacy and benefits of the proposed methods.

This work studies the finite-time self-triggered control of networked nonlinear unmanned marine vehicle (UMV) systems with hybrid attacks. A Takagi–Sugeno (T–S) fuzzy model is constructed to characterize the nonlinear UMV systems. To save limited communication and computing resources, an intelligent self-triggered mechanism is proposed, in which the threshold of self-triggered condition is adjusted intelligently by theQ-learning algorithm. Only the current states information and the last samples are adopted to calculate the interexecution interval for the next triggered instant, and then the controller signal is updated. In light of denial-of-service attacks and deception attacks under networked environment, two Bernoulli random variables are applied to describe the random occurrence of hybrid attacks. By using the Lyapunov function, sufficient conditions for finite-time boundedness of the closed-loop UMV systems are obtained. In addition, a collaborative design method for triggered parameter and controller gain is proposed. Finally, the benchmark UMV systems are simulated to demonstrate the effectiveness of the proposed strategy.

In this article, the resilient secure synchronization of complex networks (CNs) that are subject to denial-of-service (DoS) attacks is studied. In contrast to existing logic processors, a new processor in which more essential information on DoS attacks, such as the number of sampling instants being attacked and attack moment being detected, can be captured is designed. Based on the benefits of the logic processor, a switching sampled-data (SD) control protocol in which different feedback gains are chosen for different attack cases is proposed. In contrast to existing control schemes, the switching SD control protocol is more flexible. Subsequently, according to different attack cases, a switching Lyapunov-Krasovskii function (LKF) that can effectively fulfil the switching SD control protocol is founded. New resilient secure synchronization criteria that can successfully counteract the effects of DoS attacks are then established for CNs based on the switching SD control protocol and switching LKF. Finally, a complex Chua’s circuit system is used to give evidence of the feasibility and superiority of the proposed method.

This article focuses on stabilizing highly nonlinear hybrid stochastic complex dynamical networks with time delays (HSCNT) via sampled-data control with asynchronously periodic self-triggered intermittent decentralized strategy (SCAPI). Considering the constraints imposed by linear growth conditions in real models, we introduce high nonlinearity into the networks. Moreover, considering the possibility of nonsimultaneous transmission of control information by the controllers, the asynchronous decentralization is thus brought in periodic self-triggered intermittent control for the first instance. Then, to resolve the problems caused by asynchronous decentralization, a graph-theoretic approach is employed. Combining this with an auxiliary timer, an innovative Lyapunov functional is designed, after which an original Lyapunov-based method is proposed to cope with the complexity of introducing high nonlinearity and asynchronous decentralization into networks. In particular, some numerical simulations are provided for demonstration.

In the article, a stabilized issue for complex-valued hybrid stochastic multilinks networks (CHSMNs) is investigated under event-triggered control (E-TC). In contrast to existing literature, the proposed E-TC defines the event-triggered instant sequence as the random sequence rather than the number sequence, which is more accurate concerning hybrid stochastic systems. Especially, a modified more general event-triggered function is proposed. After the Zeno behavior is eliminated by the contradiction, several stabilized conditions are established to guarantee the input-to-state stability and exponential stability in the mean square and almost surely exponential stability of CHSMNs, where studying in the complex domain and no splitting real and imaginary parts. Subsequently, we analyze the lower bound of the event-triggered intervals for E-TC in the sense of mathematical expectation. Whereafter, the stabilized problem for the Cohen–Grossberg neural networks (CSDLCNs) and the islanded micro grid model (IMGM) is considered as an application. Meanwhile, numerical simulations are presented for verification.

This paper explores the dissipative fault detection filtering (FDF) issue for unmanned surface vehicles (USVs) within a network environment, considering switching channels and random occurrence of packet losses. Specifically, a multi-channel transmission mechanism is adopted to enhance the reliability of the system, and the switching channel is orchestrated by a Markov chain. The random intermittent data dropouts in each channel between the system and FDF modeled as a Bernoulli process are considered. Leveraging the Lyapunov theory and slack matrix technique, switching-channel-dependent FDF is crafted to detect faults for USVs and ensure that the augmented system keeps stochastic stability while attaining a strictly dissipative performance. Finally, an illustrative example is presented to validate the effectiveness of the proposed method for USVs.

This paper investigates the cosensus for a class of nonlinear multi-agent systems. As one of the boundless delays, proportional delay is introduced by considering the existence of tremendous amount of parallel information transfer paths with different axon sizes or lengths. To better reduce the complexity of analysis, three different parameter functions are constructed to deal with the proportional delayed systems. In view of the limited bandwidth and fully distributed architecture of agents in most practical applications, the distributed edge-based self-triggering impulsive (DEBSTI) controller is elaborately designed to eliminate the continuous monitoring, high control costs and global information transmission. Sufficient conditions are derived for achieving the consensus globally and exponentially by utilizing the methods of parameter variation, comparison principle, and average impulsive interval. In addition, the Zeno behavior could be avoided. Furthermore, a larger value range for the impulsive-effect-related parameter taking is obtained by considering the diverse functions of the impulses. Correspondingly, different convergence rates based on different ranges are calculated precisely. Finally, twenty one-link manipulators actuated by twenty DC motors are discussed in numerical simulation to demonstrate the effectiveness of the proposed results.Note to Practitioners—Nonlinear multi-agent systems could be applied to simulate large-scale systems like UAV formation and one-link manipulator with revolute joints. By considering the continuous monitoring, high control costs and global information transmission, the DEBSTI controller is skillfully designed. In view of the tremendous amount of parallel information transfer paths with different axon sizes or lengths, the proportional delay is taken into consideration to make results more practical based on three different parameter functions. Moreover, different impulsive functions are discussed. In numerical simulation, the one-link manipulator with revolute joints actuated by a DC motor is modeled to reflect the industrial application scenarios.

This paper is concerned with noisy sampled-data networked control systems with stochastic network-induced delays and noisy sampling intervals using a stochastic discrete integral quadratic constraint (DIQC) approach. Initially, the noisy sampled-data networked control system is discretized into a stochastic discrete-time system. This system is then transformed into a feedback interconnected system with zero initial conditions. Next, a stochastic DIQC method is developed to obtain a novel stability condition for the noisy sampled-data networked control system. Based on this criterion, a two-stage algorithm is presented to design suitable controllers to ensure the closed-loop system under study is uniformly exponentially stable. Finally, a numerical examples and a fourth-order interleaved flyback modular integrated converter circuit are given to validate the efficiency of the proposed results.

The observer-based stabilization of network-based interval type-2 (IT2) semi-Markov jump models is investigated, where a new control strategy based on redundant channels is proposed to overcome the adverse effects of packet loss. The IT2 fuzzy method is adopted to describe the nonlinear objects, which overcomes the uncertainty problem of the traditional T-S fuzzy model. An observer-based control strategy is adopted due to the difficulty of obtaining complete state information under a complicated network environment. The main novelty is that redundant channels are adopted to construct a suitable observer-based feedback control scheme, and the auxiliary variable is introduced to solve the matrix dimension problem to achieve better dynamic performance of IT2 fuzzy networked semi-Markov jump models, improve the stability and anti-interference ability of the system, and overcome the difficulty caused by potential packet loss. Based on the semi-Markov kernel, IT2 fuzzy, and the Lyapunov functions dependent on the elapsed time, sufficient criteria are constructed for the considered system to ensure the$\sigma$-error mean-square stability. Furthermore, the expected solvability is constructed for an observer-based feedback controller under a linear matrix inequality framework. Ultimately, the tunnel circuit model verifies the proposed control method.

This paper investigates the problem of stability analysis for aperiodic sampled-data load frequency control system with multiple time-varying delays. A discrete-time model of the load frequency control system including both generation units and energy storage systems is constructed firstly. Then, the form of feedback interconnection composing of nominal linear system and uncertainty is bulit, where uncertain operators are found to describe the aperiodic sampled-data interval and time-varying delays. Next, a uniformly exponential stability criterion is developed utilizing integral quadratic constraint analysis. Based on the stability criterion, algorithms are presented to obtain more accurate delay margins and maximum allowable sampled-data intervals. Finally, case studies are conducted to demonstrate the validity and benefits of proposed stability criterion.

This paper proposes a novel dynamic event-triggered mechanism (DETM) of a switched linear DC microgrid with intermittent denial-of-service (DoS) attacks. In a switched linear system based on DETM, the subsystem mode is immediately updated along with the occurrence of switching signals, while the controller mode is updated after a successful event-triggering. This means both communication interruptions and periodic sampling may cause asynchronous switching situations, which together pose a challenge for stability analysis. To this end, a multiple Lyapunov function that can simultaneously describe subsystem mode and controller mode is adopted, and a new DETM based on acknowledgement character technique (ACK) is studied to ensure input-to-state stability (ISS) of the system. Meanwhile, the maximum allowed DoS duration is first studied to further analyze the relationship between DoS parameters, event-triggered parameters, and system stability. By utilizing ACK, the system can detect the end of the DoS attack and remain ISS under dynamic event-triggered control. Finally, simulation verification is conducted in a switched DC microgrid.

In this paper, the imitation reinforcement learning-based control problem is studied for discrete-time Markov jump systems with external disturbances. First, zero-sum game method is introduced to deal with external disturbances, where control input and external disturbances are regarded as two rival players in adversarial environments. Then, the imitation reinforcement learning problem is formulated, where learner Markov jump systems aim to learn the optimal behavior of expert Markov jump systems. Considering that the dynamics information of both learner systems and expert systems is accurately known, an offline parallel imitation learning algorithm is designed for learner systems to mimic expert behaviors, which contains three steps: 1) policy evaluation, 2) search for weight matrix, and 3) policy improvement. On this basis, by observing the optimal behavior of expert systems, an online imitation reinforcement learning algorithm is presented for learner systems with completely unknown system dynamics. Moreover, rigorous proofs of convergence and stability analysis are provided to guarantee the performance of proposed algorithms. Finally, the effectiveness of the proposed method is verified by a single-machine infinite-bus power systems.

This article addresses adaptive optimized tracking control problem for strict-feedback cyclic switched nonlinear output constrained systems with average cyclic dwell time (ACDT). Different from most of the existing results on optimized control of switched nonlinear systems, a new mode-dependent reinforcement learning (RL) algorithm of identifier-critic-actor architecture is designed. Technically, with the help of neural networks (NNs) switched state observer, the virtual and actual optimal controllers are developed by solving the Hamilton-Jacobi-Bellman (HJB) equation. Meanwhile, to reduce the impact of systems switching on the overall optimization control, the information of the switching signal is considered into the optimal performance index functions. In an attempt to settle the output constraints, a nonlinear output-dependent time-varying function is used to ensure that the system output never transgresses the prescribed regions. More importantly, by combining the improved ACDT method and Lyapunov stability theorem, a novel adaptive optimized control scheme is put forward which ensures that the boundedness of all signals in the closed-loop system. Finally, the effectiveness of the proposed optimized control algorithm is verified by numerical as well as practical simulations.

In this paper, the adaptive intelligent control problem for a class of nonlinear switched stochastic cyber-physical systems (CPSs) with output constraint under deception attacks is studied. The output-dependent function is introduced to convert the system into an unconstrained system. In order to eliminate the effects caused by the attacks, the attack gains are introduced into the coordinate transformations and then the Nussbaum function technology is used to deal with the unknown attack gains. Based on the new coordinate transformations, the corresponding controllers are designed for each subsystem using the compromised states. Furthermore, a switching event-triggered mechanism (ETM) is proposed, which can not only resolve asynchronous switching problem without any strict restrictions, but also effectively save communication resources. Meanwhile, a segment constant variable is introduced into the ETM, which is helpful to find a strict positive lower bound for two consecutive triggering intervals to obtain the absence of Zeno behavior. It is shown from the Lyapunov stability theory that all signals in the closed-loop system are bounded in probability under arbitrary switching. Finally, the simulation results validate the effectiveness of the proposed method.

This article proposes a novel event-triggered second-order sliding mode (SOSM) control algorithm using the small-gain theorems. The developed algorithm has global event property in aspects of the triggering time intervals. First, an SOSM controller is designed related to the sampling error of states, and it is proved that the closed-loop system is finite-time input-to-state stable (FTISS) with the sampling error via utilizing the small-gain theorems. Second, combined with the constructed SOSM controller, a new triggering mechanism is proposed depending on the sampling error by designing the appropriate FTISS gain condition. Third, the practical finite-time stability of the closed-loop system is verified. It is shown that the minimum triggering time interval is always a positive value in the whole state space. Finally, the simulation results demonstrate the effectiveness of the developed control method.

This article focuses on solving the secure control problem by developing a novel resilient hybrid learning scheme for discrete-time Markov jump cyber-physical systems with malicious attacks. Within the zero-sum game framework, the secure control problem is converted into solving a set of game coupled algebraic Riccati equations. However, it contains the coupling terms arising from the Markov jump parameters, which are difficult to solve. To address this issue, we propose a framework for parallel reinforcement learning. Thereafter, a model-based resilient hybrid learning scheme is first designed to obtain the optimal policies, where the system dynamics are required during the learning process. Furthermore, a novel online model-free resilient hybrid learning scheme combining the advantages of value iteration and policy iteration is proposed without using the system dynamics. Besides, the convergence of the proposed hybrid learning schemes is discussed. Eventually, the effectiveness of the designed algorithms is demonstrated with the inverted pendulum model.

The model predictive control (MPC)-based asynchronous attack tolerant control scheme is investigated in this article for uncertain Markov jump cyber-physical systems (MJCPSs) under the Denial-of-Service (DoS) attack. To tackle the problem of the system running mode may not be observed in the control center, an asynchronous model predictive controller is proposed. Specifically, a dynamic controller, which can tune the performance online, is designed besides a traditional state feedback one. Even though such a combination may cause possible degradation of system performance, it can expand the initial feasible region and relieve the online computation burden efficiently. In addition, a decision variable is introduced to alleviate limitations on the feasible region generated by the constraints in the traditional MPC method. A series of solvable optimal problems are further constructed to achieve the desired performances. Finally, an application of the proposed method is given to demonstrate its effectiveness.

This article presents an event-triggered neural-network (NN) tracking control scheme, capable of ensuring transient performance for switched nonlinear systems. A mode-dependent event-triggered communication mechanism (MDETCM) is designed, and this significantly saves communication resources without limiting the number of switches between two consecutive triggering instants. Meanwhile, to solve the impact of asynchronous switching on system performance, the information of the switching signal is considered into the event-triggered mechanism (ETM). Also, by introducing a normalized function transformation and a tan-type barrier function, the transient performance is regarded as a tracking error constraint without considering the initial conditions of system output and reference signal required in traditional prescribed performance bound (PPB) control. At the same time, the improved admissible edge-dependent average dwell time (AED-ADT) method is cleverly connected with adaptive backstepping control, and a state-feedback tracking algorithm is proposed, under which all closed-loop signals are bounded and the transient performance of the controlled plant is ensured. Finally, the superiority of the proposed scheme is demonstrated through numerical studies, and the control scheme is available for a single-link robot.

A novel reinforcement learning-based predefined-time tracking control scheme with prescribed performance is presented in this article for nonlinear systems in the presence of external disturbances. First, by employing the backstepping strategy, an adaptive optimized controller is developed under the identifier-critic-actor framework. Therein, the unknown nonlinear dynamics and the system control behavior can be learned effectively through neural networks. Moreover, aiming at obtaining the preset tracking performance, the prescribed performance control is integrated with the predefined-time control. In contrast to previous studies, the proposed scheme can not only constrain the tracking error rapidly to a prearranged vicinity of origin, but also ensure that the upper bound of convergence time can be adjusted in advance via a separate control parameter. In terms of the predefined-time stability theory, the boundedness of all system states can be proven within a predefined time. Finally, the availability and improved performances of the proposed control scheme are demonstrated by a numerical example and a single-link manipulator example.

This study mainly investigates the adaptive leader-following consensus tracking control problem for a class of nonlinear multiagent systems (MASs) subjected to unknown control directions, external disturbances, and sensor deception attacks. To start with, an equivalent MAS with known control directions is obtained by introducing a linear state transformation. For the purpose of estimating the unavailable system states caused by malicious attacks, a quantization-based fuzzy state observer is designed, and the fuzzy-logic system (FLS) is utilized to approximate nonlinear functions. Moreover, a dynamic uniform quantizer with scaling function is established to reduce information transmission. With the help of coordinate transformation and available compromised states, a novel compensation mechanism is designed to offset the influence of filter errors while avoiding the problem of "explosion of complexity" in the backstepping design process. In addition, the Nussbaum-type function is considered to eliminate the design obstacle of unknown control gains resulting from the attacks. Under the constructed consensus protocol, it is proved theoretically that the consensus tracking error converges to an adjustable small neighborhood of the origin, and all signals in the closed-loop system are bounded. Finally, the feasibility of the provided secure control scheme is verified through two simulation examples.

When there is a sudden load disturbance in an islanded microgrid, the peer-to-peer control model requires the energy resource to maintain a margin of generation, resulting in a relatively limited regulation range, that is, voltage/frequency sometimes requires additional control to maintain stability. A "source-storage-load" coordinated master-slave control strategy is proposed in this study to address the aforementioned issues. The system voltage and frequency will be stable as long as the output frequency and voltage of the master resource are stable. Furthermore, it can fully utilize the power supply capacity of resources to support the supply-demand balance. The following tasks are included in the proposed strategy: 1) to improve the operational security in the face of load disruption, a source-storage-load coordinated control method based on the "ramping speed" ratio is proposed, which can quickly restore the balance of supply and demand; 2) to improve the communication reliability in the face of interruption, a channel planning method is proposed, which can address the communication interruption problem by constructing an internal network among source-storage-load; and 3) to improve the mode switching stability of resources subjected to external disturbance, the external disturbance suppression and stability analysis involved in the regulation process are completed using sliding-mode control and small signal model methods. Related case studies are carried out to verify the effectiveness of the proposed strategies.

For the flexible riser systems modeled with partial differential equations (PDEs), this article explores the boundary control problem in depth for the first time using a dynamic event-triggered mechanism (DETM). Given the intrinsic time-space coupling characteristic inherent in PDE computations, implementing a state-dependent DETM for PDE-based flexible risers presents a significant challenge. To overcome this difficulty, a novel dynamic event-triggered control method is introduced for flexible riser systems, focusing on optimizing available control inputs. In order to save computational costs from the controller to the actuator, a dynamic event-triggered adaptive boundary controller is designed to effectively reduce boundary position vibrations. Additionally, considering external disturbances, an adaptive bounded compensation term is incorporated to counteract the influence of external disturbances on the system. Addressing boundary position constraints, a new integral barrier Lyapunov function (iBLF) tailored specifically for flexible riser systems is introduced, thereby alleviating conservatism in the controller design of flexible risers modeled by PDEs. At last, the validity of the proposed method is demonstrated through a simulation example.

This article investigates the fully distributed fuzzy adaptive consensus problem for nonlinear multi-agent systems (MASs) with an active leader. The active leader can be controlled in a real-time manner by an outside operator, and the control signals of the leader and its upper bound are completely unknown to all followers. The problem is studied in both state feedback and output feedback cases. In the case where the system states are available, a fuzzy adaptive controller that can dynamically compensate for the approximation errors of fuzzy logic systems (FLSs) is designed for the first time. This reduces the high demand for fuzzy approximation accuracy while maintaining the control performance. In another case where only the disturbed system output is available, an augmented observer that can handle arbitrary unknown measurement disturbances is novelty constructed. In both cases, by introducing adaptive auxiliary functions, unknown leader inputs are effectively disposed of, and the tracking performance of the system is nicely guaranteed. Finally, simulation examples are given to illustrate the feasibility and advantages of the developed strategies.

In this article, we investigate an improved dynamic guaranteed cost event-triggered-based anti-disturbance control for Takagi–Sugeno fuzzy wind-turbine systems subject to external disturbances. A guaranteed cost event-triggered paradigm with dynamic threshold and sector structure is constructed to alleviate unnecessary triggers caused by outlier measurement. An additional event condition is designed to deal with the difference of premise variable between the system and controller. A PI-type intermediate estimator is introduced to simultaneously estimate the system state and external disturbance. Subsequently, an event-triggered fuzzy controller is built to actively compensate the external disturbances. With the help of Finsler's lemma, sufficient criteria are derived in terms of linear matrix inequalities to make the wind-turbine systems asymptotically stable. Finally, the proposed method is verified by comparative studies.

This article investigates the dynamic-memory event-triggered (DMET)-based double asynchronous dissipative control issue for Takagi–Sugeno fuzzy semi-Markov jump systems (T-S FSMJSs) under redundant channel strategy. To mitigate the network burden and improve the system control performance, a new DMET mechanism is skillfully developed by adopting weighted past released packets of measured output and dynamically adjusted thresholds. Due to the impact of network environment, a novel framework of DMET-based controller scheme is constructed by considering the premise variable asynchronous and mode asynchronous phenomena between the plant and controller simultaneously. The imperfect premise matching and hidden semi-Markov model approaches are employed to resolve two types of mismatches. The redundant channel communication strategy with quantization measurement is used to improve the nonfragility of signal transmission. In terms of the stochastic theory and fuzzy model technique, some criteria are forwarded to ensure the stochastic stability and strict dissipative performance for closed-loop T-S FSMJSs. By the matrix decoupling method, the control scheme is established. Lastly, a single-link robot arm system example is presented to certify the effectiveness of the developed result.

This article aims to develop a methodology for the hybrid-triggered control for nonlinear switched systems under denial-of-service attacks. By means of the T–S fuzzy method, the complex nonlinear system is transformed into a series of local linear models. Essentially different from the common switching signal design for DoS attacks, it challenges the situation that attack signals may appear with high frequency in a certain interval. The phenomenon aroused by attacks and the characteristics of the switched system constitute a double-layer switched structure, both of the switchings described by the PDT switching rule. A hybrid control strategy based on event-triggered mechanism is utilized to reduce the frequency of data transmission, where signal updates of the controller exhibit aperiodic. By constructing a Lyapunov function depending on dual switching signals, sufficient conditions to ensure the global exponential stability of the considered system are deduced. Furthermore, the minimum interval between two successful events under the event-triggered mechanism is calculated, which proves that the Zeno behavior can be avoided. Finally, a simulation example and an example of the single-link robot arm are studied, illustrating the proposed method's feasibility and effectiveness.

This article develops a method to address the observer-based fuzzy control for a class of discrete-time nonlinear networked control systems under multichannel attacks, in which taking semi-Markov process to characterize the switching among various attack threats on multiple communication channels. The nonlinear networked control systems are modeled by a Takagi–Sugeno fuzzy model, where the membership function of the controller depends on the estimated state after observation. Moreover, for solving the problem of inaccessible actual attack modes, an observed-mode sequence driven by emission probabilities is adopted. In the framework of the hidden semi-Markov process with a double-layer stochastic structure, the analysis for$\varepsilon$-error mean square stability and conditions for observer/controller synthesis of the resultant system are obtained via a Lyapunov function relied on both real and estimated attack modes. Finally, a simulation of the nonlinear mass-spring-damper mechanical system is conducted, demonstrating the proposed method's feasibility.

This article addresses the stability and optimal control problem (OCP) for nonlinear Itô stochastic Markov jump systems (NISMJSs) by employing the Takagi–Sugeno (T–S) fuzzy model. First, the NISMJSs undergo transformation through T–S fuzzy model into a class of Itô stochastic Markov jump fuzzy systems (ISMJFSs), which is a composite of a set of linear subsystems. Then, based on the Lyapunov stability theorem, a sufficient condition is provided for establishing the mean-square stability of ISMJFSs. For solving the OCP, the most commonly used method is policy iteration (PI), but it requires an initial stabilizing control policy to be given, which relies on precise information about the system dynamics. To remove this limitation, we introduce hybrid iteration (HI) to design the optimal controller for the Itô stochastic systems for the first time without requiring the initial stabilizing control gain, and then the stability and convergence of the offline HI algorithm for the ISMJFSs are proven. Moreover, by incorporating reinforcement learning (RL) techniques, we develop an online HI algorithm to successfully solve the OCP for ISMJFSs with unknown system dynamics matrices, which initializes the algorithm without the need for a set of initial stabilizing control gain matrices. Finally, the efficiency of the proposed methods is illustrated through simulation results using the RLC electric circuit.

This paper investigates the sliding mode control (SMC) issue for discrete nonlinear semi-Markovian switching singularly perturbed models with incomplete semi-Markovian kernels (SMKs). A T-S fuzzy model is adopted to characterize the underlying model with singularly perturbed parameters, which is more widely used in nonlinear systems. Because the statistical characteristic of a semi-Markovian kernel is laborious to be obtained in practical engineering, the information of semi- Markov kernel is recognized to be incomplete. The main novelty lies in constructing a suitable fuzzy SMC scheme to realize the attainment of the quasi-sliding mode, overcoming the difficulty caused by incomplete SMK. Based on the Lyapunov function related to the fuzzy rule and the elapsed time of current mode, the stability criterion is constructed for the corresponding model. An appropriate SMC approach is then developed to achieve the attainment of a quasi-sliding mode. Finally, the theoretical results are verified by an improved tunnel diode circuit model.

This article is focused on the problem of adaptive event-triggered-based security controller construction for nonlinear networked control systems under multiple network attacks, which are represented by interval type-2 fuzzy models. First, in an attempt to mitigate the communication load, an enhanced adaptive event-triggered mechanism is utilized for determining the signals' transmission order, which is capable of adjusting the threshold dynamically with the signals probabilistically subjected to occurring malicious attacks. In addition, the observer is constructed under unfathomable premise variables, and then the controller with imperfect matching membership functions is created based on the estimated states, which is homogenous polynomially parameter-dependent. Moreover, the observer-based controller is obtained for the asymptotic stability with an$H_{\infty }$performance index via the Lyapunov stability theory. Then, sufficient conditions for the appropriate observer and controller gain matrices are given based on the linear matrix inequality method. Subsequently, the reliability of the proposed observer-based security fuzzy control design approach is demonstrated by two numerical simulation examples.

In this paper, a novel reinforcement learning-based predefined-time control method for nonlinear servo systems with prescribed performance is proposed under an event-triggered strategy. Firstly, the nonlinear dynamics and control behaviors of the systems can be trained effectively through fuzzy logic systems under the identifier-critic-actor framework. Moreover, by employing the prescribed performance control and a switching event-triggered rule, system tracking performance can be ensured while decreasing the data transmission frequency. With the assistance of the predefined-time stability criteria, the boundedness of system variables and the convergence of tracking errors within a predetermined time can be guaranteed. Comparisons with some existing control schemes are addressed regarding tracking performance and action costs. The availability and superiority of the suggested scheme are verified in the simulations.

This paper aims to develop a dynamic-memory event-triggered mechanism (DMETM) for Takagi-Sugeno (T-S) fuzzy switched systems under denial-of-service (DoS) attacks. Persistent dwell-time (PDT) is used to describe the characteristics of both DoS attacks and switching behaviours. The usage of PDT allows high-frequency switching and attacking phenomenon during certain intervals. Meanwhile, the DMETM is designed to screen for more effective triggering instants and reduce signal transmission frequency. The dynamic triggering conditions vary based on both the latest triggering data and historical triggering data simultaneously. By combining the DMETM and the acknowledgement character technique (ACK), the system can detect the initial and end instants of DoS attacks to improve control performance. Besides, a dual Lyapunov function is constructed and sufficient conditions for input-to-state stability (ISS) of nonlinear switched systems (NSSs) are derived. Finally, simulation verification is conducted in a nonlinear DC microgrid, illustrating feasibility and effectiveness of the DMETM.

This article is centered on the synchronization issue of fuzzy reaction–diffusion dynamic networks (RDDNs) with multiple cyberattacks. A novel attack-resilient dynamic event-triggered policy and the relevant fuzzy attack-resilient controller are designed via considering the deception and irregular (aperiodic) denial-of-service (DoS) attacks simultaneously. Based on a novel piecewise Lyapunov–Krasovskii functional with the realistic sampling information and some inequality techniques, some criteria are derived to ensure that the synchronization of fuzzy RDDNs can be realized in the presence of the deception and irregular DoS attacks. Lastly, simulation examples containing the colour image encryption/decryption are supplied to exhibit the advantages and practicality of the developmental theories.

This article focuses on fuzzy structural adaptive optimal control issue of discrete-time nonlinear complex networks (CNs) via adopting the reinforcement learning (RL) and Takagi–Sugeno fuzzy modeling approaches, where the control gains are subjected to structured constraints. In accordance with the Bellman optimality theory, the modified fuzzy coupled algebraic Riccati equations (CAREs) are constructed for discrete-time fuzzy CNs, while the modified fuzzy CAREs are difficult to solve directly through mathematical approaches. Then, a model-based offline learning iteration algorithm is developed to solve the modified fuzzy CAREs, where the network dynamics information is needed. Moreover, a novel data-driven off-policy RL algorithm is given to compute the modified fuzzy CAREs, and the structural optimal solutions can be obtained directly by using the collected state and input data in the absence of the network dynamics information. Furthermore, the convergence proofs of the presented learning algorithms are provided. In the end, the validity and practicability of the theoretical results are explicated via two numerical simulations.

This note proposes a novel weighted memory$H_\infty$controller for time-varying delayed Takagi–Sugeno fuzzy systems via a distributed delay method. In contrast to the traditional memory controller based on instantaneous historical data, the continuous historical information over a given period is utilized to construct the fuzzy-based memory controller, in which a weighting function is employed for the first time to describe the importance of historical information. Then, the weighted historical information is expressed as a distributed delay and the weights are represented as delay kernel. By using a less conservative integral inequality with respect to the weighted historical information, original sufficient conditions are deduced to solve the fuzzy weighted memory$H_\infty$controller gains. Finally, simulations are carried out to reveal the merits of the presented controller.

In this article, the adaptive fuzzy optimized tracking control problem of uncertain cyclic switched stochastic nonlinear systems with actuator faults and prescribed performance is studied under average cycle dwell time. For achieving a better optimal control strategy, the information of the switching signal is taken into account by the reinforcement learning algorithm of the identifier-critic-actor architecture. Meanwhile, a novel mode-dependent event-triggered optimized mechanism for subsystem is proposed to resolve the impact of asynchronous switching on system performance, which not only does not have strict assumptions but also reduces the communication burden. Moreover, to eliminate the impact caused by actuator failures, fault-tolerant compensation scheme depending on the switching signal is designed. By presenting Lemma 3 and using the coordinate transformation technique, it is proven that the choice of the performance index function still conforms to the control protocol under the prescribed performance control framework. In the optimized backstepping control design, the proposed controller is able to ensure that all signals in the closed-loop system are bounded in probability and tracking error meets performance. Finally, both numerical and practical simulations are given to illustrate the effectiveness of the proposed optimization scheme.

This article investigates the formation control problem for nonlinear multiagent systems (MASs) exhibiting two time scale (TTS) characteristics. Initially, a fuzzy model is introduced to capture the dynamics of the original nonlinear MASs. Subsequently, a full-order singularly perturbed system is developed to model the TTS phenomenon in the nonlinear MASs. Following this, the design of controllers to realize formation control of nonlinear MASs is converted into solving a set of discounted algebraic Riccati equations. To relax the restriction of system dynamic information, a novel off-policy integral reinforcement learning scheme is adopted to design the controllers online. Finally, a simulation example is provided to demonstrate the efficacy of the proposed algorithm.

This article addresses the quantized asynchronous filtering issue for semi-Markov switching systems described by the Takagi–Sugeno fuzzy model with multiple stochastic delays under deception attacks. First, considering communication congestion and the randomly changing environment, multiple stochastic time-varying delays are introduced via the Bernoulli distribution. Second, a mode-dependent quantizer is proposed to save the limited network bandwidth. Third, to enhance network security, the measurement output signal is assumed to suffer from deception attacks during the transmission process. Subsequently, under the influences of the above-mentioned three factors, a novel filtering error model is well constructed. Further, by applying the Lyapunov–Krasovskii stability theory and the stochastic analysis techniques, a delay-dependent stochastic stability criterion with less conservatism is given to ensure that the filtering error system is stochastically stable with a prescribed$\mathcal {H}_{\infty }$performance, where the feasible asynchronous filter gains and dynamic parameters can be obtained in a unified framework. Ultimately, two examples are employed to demonstrate the availability of the designed filter method.

Most computation-intensive industry applications and servers encounter service-reliability challenges due to the limited resource capability of the edge. Achieving quality data fusion and accurate service reliability with optimized service-x execution cost is challenging. While existing systems have taken into account factors such as device service execution, residual resource ratio, and channel condition; the service execution time, cost, and utility ratios of requested services from devices and servers also have a significant impact on service execution cost. To enhance service quality and reliability, we design a 2-step adaptive service-X cost consolidation (ASXC2) approach. This approach is based on the node-centric Lyapunov method and distributed Markov mechanism, aiming to optimize the service execution error rate during offloading. The node-centric Lyapunov method incorporates cost and utility functions and node-centric features to estimate the service cost before offloading. Additionally, the Markov mechanism-inspired service latency prediction model design assists in mitigating the ratio of offload-service execution errors by establishing a mobility-correlation matrix between devices and servers. In addition, the non-linear programming multi-tenancy heuristic method design help to predict the service preferences for improving the resource utilisation ratio. The simulations show the effectiveness of our approach. The model performance is enhanced with 0.13% service offloading efficiency, 0.82% rate of service completion when transmitting data size is 400 kb, and 0.058% average service offloading efficiency with 40 CPU Megacycles when the vehicle moves 60 Km/h speed around the server communication range. Our model simulations indicate that our approach is highly effective and suitable for lightweight, complex environments.

This article studies the input-to-state stability (ISS) problem of cyber–physical systems (CPSs) subject to unknown disturbance and malicious denial-of-service (DoS) attacks. A resilient event-triggering mechanism is first proposed to reduce unnecessary network resource consumption. Then, a novel predictive control protocol is put forward to skillfully compensate for lost signals of CPSs during the case of DoS attacks. It is shown that the resulting closed-loop CPSs can achieve ISS under the proposed control update protocol. One of the key advantages of our proposed control protocol is that the missing information can be compensated, and the CPSs thus, can tolerate more unfavorable DoS attacks. Moreover, an intuitive minimum interevent time is provided so as to exclude the Zeno execution of control signals. Finally, a simulation example on unmanned ground vehicle is presented to verify the effectiveness of the proposed control protocols.

This paper addresses the problem of adaptive memory-event-triggered control for intelligent vehicle transportation systems (IVTSs). Unlike the existing methods for modeling IVTSs, the acceleration information is introduced in the desired distance to achieve better distance control performance of IVTSs. The information between vehicles interacts over a wireless network. In order to further reduce the amount of data transmission, a novel adaptive memory-event-triggered mechanism (METM) is developed, in which historical information is utilized. In addition, the proposed METM threshold is designed to vary with the system state of each autonomous vehicle to adjust the data-releasing rate adaptively. Under the adaptive METM, a distributed tracking controller with historical information is put forward to guarantee uniformly ultimately bounded (UUB) stability using the Lyapunov stability theory and linear matrix inequality (LMI) technique. Finally, simulation results are given to verify the superiority of the proposed method.

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target’s object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

This study concentrates on the finite-time protocol-based stabilization for networked unmanned marine vehicle (UMV) systems with denial-of-service (DoS) attacks. To save limited communication resources, a novel event-driven protocol is proposed, in which Q-learning mechanism is adopted to adjust the threshold of the driven condition intelligently. In light of aperiodic DoS attacks, the corresponding switched system is constructed in light of the average dwell time strategy. Employing switching Lyapunov functional, sufficient conditions are made for finite-time boundedness of the UMV systems with disturbance, respectively, in which the relationship among the DoS parameter, the triggered parameter, the finite-time parameter, and the sampling period is accurately characterized. Moreover, the co-design approach of driven parameter and controller gain is proposed for the corresponding UMV systems. Finally, the benchmark UMV systems are shown to verify the effectiveness of the proposed strategy.

It is challenging to ensure car driving comfort and safety under denial-of-service (DoS) jamming attacks. For this reason, this article proposes a resilient-based and attack-intensity adaptive event-triggered control scheme to study the$\mathcal{H}_{\infty}$controlled networked active quarter-vehicle suspension systems (QVSSs), which reduces the frequency of controller updates and provides robust resistance to DoS attacks. It is shown that if the tendency of DoS attack intensity increases, more communication signals can be transmitted to the controller. Conversely, if the tendency of DoS attack intensity decreases, fewer communication signals are being utilized. Using the novel two-side looped-functional method and some technical lemmas, sufficient conditions for the exponential stability and$\mathcal{H}_{\infty}$control performance of QVSSs are obtained. Simulation results indicate that the suggested control approach enhances driving comfort and safety while reducing the data transmission burden.

This paper investigates the fusion-based event-triggered$H_\infty$state estimation of autonomous surface vehicles (ASVs) against measurement outliers and cyber-attacks. To release communication burden, a novel fusion-based event-triggered mechanism (FETM) dependent on the fusion of historical system outputs is proposed. There exist two advantages of this mechanism: 1) the use of fusion signal is able to avoid the information loss between two sampling instants and reduce the redundant triggering events resulted from system disturbances and noises; 2) by requiring the error signal in the triggering condition not only lager than a lower threshold but also less than an upper threshold, the false triggering events incurred by measurement outliers also can be discarded. Then, a time-varying delay system is established to represent the event-triggered$H_\infty$state estimation error system with network-induced delays. Then, sufficient conditions are deduced for solving$H_{\infty}$estimator gain and triggering matrix of FETM. Lastly, some simulation results are given to illustrate the merits of the theoretical method.

In this article, the quasi-synchronization for a kind of coupled neural networks with time-varying delays is investigated via a novel event-triggered impulsive control approach. In view of the randomly occurring uncertainties (ROUs) in the communication channels, the global quasi-synchronization for the coupled neural networks within a given error bound is considered instead of discussing the complete synchronization. A kind of distributed event-triggered impulsive controllers is presented with considering the Bernoulli stochastic variables based on ROUs, which works at each event-triggered impulsive instant. According to the matrix measure method and the Lyapunov stability theorem, several sufficient conditions for the realization of the quasi-synchronization are successfully derived. Combining with the mathematical methodology with the formula of variation of parameters and the comparison principle for the impulsive systems with time-varying delays, the convergence rate and the synchronization error bound are precisely estimated. Meanwhile, the Zeno behaviors could be eliminated in the coupled neural network with the proposed event-triggered function. Finally, a numerical example is presented to prove the results of theoretical analysis.

This article investigates the distributed leader-following consensus for a class of nonlinear stochastic multiagent systems (MASs) under directed communication topology. In order to estimate unmeasured system states, a dynamic gain filter is designed for each control input with reduced filtering variables. Then, a novel reference generator is proposed, which plays a key role in relaxing the restriction on communication topology. Based on the reference generators and filters, a distributed output feedback consensus protocol is proposed by a recursive control design approach, which incorporates adaptive radial basis function (RBF) neural networks to approximate the unknown parameters and functions. Compared with existing works on stochastic MASs, the proposed approach can significantly reduce the number of dynamic variables in filters. Furthermore, the agents considered in this article are quite general with multiple uncertain/unmatched inputs and stochastic disturbance. Finally, a simulation example is given to demonstrate the effectiveness of our results.

This work investigates the protocol-based synchronization of inertial neural networks (INNs) with stochastic semi-Markovian jumping parameters and image encryption application. The semi-Markovian jumping process is adopted to characterize INNs under sudden complex changes. To conserve the limited available network bandwidth, an adaptive event-driven protocol (AEDP) is developed in the corresponding semi-Markovian jumping INNs (S-MJINNs), which not only reduces the amount of data transmission but also avoids the Zeno phenomenon. The objective is to construct an adaptive event-driven controller so that the drive and response systems maintain synchronous relationships. Based on the appropriate Lyapunov functional, integral inequality, and free weighting matrix, novel criteria are derived to realize the synchronization. Moreover, the desired adaptive event-driven controller is designed under a semi-Markovian jumping process. The proposed method is demonstrated through a numerical example and an image encryption process.

In this article, the dynamic event-triggered control problem of memristive neural networks (MNNs) under multiple cyber-attacks is considered. A novel dynamic event-triggering scheme (DETS) and the corresponding event-triggered controller are proposed by taking into consideration both denial-of-service and deception attacks (DoS-DAs). Then, a key lemma is established to show that the dynamic event-triggered controller can be used to solve the globally stochastically exponential stability (GSES) issue of concerned MNN under multiple cyber-attacks. Meanwhile, a novel Lyapunov functional is proposed based on the actual sampling pattern. It is shown that under our proposed dynamic event-triggered controller and Lyapunov functional, the concerned MNN can achieve GSES in the presence of DoS-DAs. In addition, our results include relevant results on event-triggered control of MNN with static event-triggering scheme (SETS) or without cyber-attacks as special cases. The effectiveness of the proposed event-triggered controller under multiple cyber-attacks is illustrated by a simulation example.

This article investigates the problem of event-based intermittent formation control for multi-UAV systems subject to deception attacks. Compared to the available research studies on multi-UAV systems with continuous control strategy, the proposed intermittent control strategy saves a large amount of computation resources. An average method is introduced in developing the event-triggered mechanism (ETM) such that the amount of unexpected triggering events induced by uncertain disturbances is greatly reduced. Moreover, such a mechanism can further decrease the average data-releasing rate, thereby alleviating the burden of network bandwidth. Sufficient conditions for multi-UAV systems with deception attacks to achieve the predefined formation are obtained with the aid of Lyapunov stability theory. Finally, the validity of the proposed theoretical results is demonstrated via a simulation example.

State estimation issue is investigated for a switched complex network (CN) with time delay and external disturbances. The considered model is general with a one-sided Lipschitz (OSL) nonlinear term, which is less conservative than Lipschitz one and has wide applications. Adaptive mode-dependent nonidentical event-triggered control (ETC) mechanisms for only partial nodes are proposed for state estimators, which are not only more practical and flexible but also reduce the conservatism of the results. By using dwell-time (DT) segmentation and convex combination methods, a novel discretized Lyapunov–Krasovskii functional (LKF) is developed such that the value of LKF at switching instants is strict monotone decreasing, which makes it easy for nonweighted$\mathcal{L}_2$-gain analysis without additional conservative transformation. The main results are given in the form of linear matrix inequalities (LMIs), by which the control gains of the state estimator are designed. A numerical example is given to illustrate the advantages of the novel analytical method.

In this work, the problem of an event-based$\mathcal {L}_{2}-\mathcal {L}_{\infty }$security control for power system load frequency control is discussed via a sliding mode control (SMC) strategy. Specifically, for responding to the Denial-of-Service attacks with limited energy, a switching-like event-triggered mechanism (ETM) is adopted, remedying the adverse impact of data losses. Meanwhile, synthesizing the robust SMC with the switching-like ETM, the sliding mode dynamics under cyber-attacks are established. Then, with Lyapunov stability theory, the sufficient criteria are deduced, ensuring that the resulting system can reach asymptotic stability with$\mathcal {L}_{2}-\mathcal {L}_{\infty }$performance. Correspondingly, the gain of an event-based load frequency SMC law, achieving the ideal system performance while assuring the reachability of sliding surface, is figured out by settling convex optimization problems. Finally, the serviceability and validity of the presented method are verified through an example with corresponding simulation results.

This article is devoted to studying the bipartite synchronization issue of multicoupled complex dynamic networks with mismatched parameters. A generalized processing analysis method for dealing with the network with derivative coupling is first presented. To acquire suitable input intervals, a novel memory-based self-triggered impulsive controller is elaborately designed. Accordingly, the triggering moments are precisely determined, which take the average error states of a small fraction of monitoring moments into account. Sufficient conditions for the bipartite synchronization are eventually obtained by utilizing the Lyapunov stability theorem in conjunction with the parameter variation approach and definition of average impulsive interval. Since time-varying impulsive effects are considered, the definition of average impulsive gain is introduced in order to estimate the convergence rates and error bounds, respectively. The capability of derived mathematical deductions is ultimately demonstrated by numerical examples. Further, comparative experiments are given to show the superiority of performance within three event-based mechanisms.

In this article, the exponential stability and controller design of large-scale networked control systems with prediction-based controller are addressed. A matrix dimension transformation method is proposed, which solves the problem of matrix dimension mismatch due to the complex structure. First, a new Lyapunov–Krasovskii functional (LKF) is introduced under the condition of the backstepping-based partial differential equation (PDE) method, and stability criteria with less conservatism are obtained with the help of free-weight matrixes and the inequality deflation technique. Second, combining with the reduction-based ordinary differential equation (ODE) method, by constructing the two-sided mode-dependent loop-based LKF, the information of sampled-data intervals can be utilized more sufficiently. Meanwhile, with the help of the proposed matrix dimension transformation method, controller design algorithms based on the two prediction methods are given, respectively. Finally, the effectiveness of theoretical analysis are demonstrated by using two coupled cart-pendulum systems, where the proposed method can obtain larger upper bounds of the allowable time delay and sampled-data intervals.

This study is to focus on the problem of finite-time control design for a class of networked control systems (NCSs) with mixed delays. First, a novel hybrid dropout compensation control method is proposed to reasonably use available system feedback information, which can actively compensate for data packet dropouts in two communication channels. Second, an improved augmented switched system model is constructed based on the mixed delays and the hybrid parameter. Then, the gain matrices that vary with the data packet dropout are formulated to ensure stochastic finite-time stability (SFTS) and improve the transient control performance of the resulting closed-loop system. The slack variables (SVs)-based algorithm is presented to reduce the computational time, and the average dwell-time switching law is determined. Finally, the obtained theoretical results are verified by a numerical example.

This article investigates the robust output regulation of linear uncertain singularly perturbed systems by output-feedback control. Due to the presence of extremely small perturbation parameters, numerical issues will arise when applying conventional robust regulator design techniques to the regular system. To handle the problem, a continuous-time output-feedback controller adopting the forwarding design technique is proposed by combining the internal model approach with singularly perturbed theory. In this manner, the circumvention of numerical issues and the resolution of robustness problems can be achieved. Additionally, the sampling of the transmissions between the plant and the controller is taken into account, leading to the proposed implementation of a dynamic output-based event-triggered strategy. As a result, the output regulation property becomes practical. To avoid Zeno behavior, the implementation ensures the existence of a strictly positive minimum intertime for the triggering instant sequence. The validity of the results is demonstrated through the presentation of a numerical example.

In this study, the partial-node-based state estimator design problem is addressed for complex networks subject to sojourn probabilities and a memory-based dynamic event-triggered protocol (DETP). Based on nonhomogeneous sojourn probabilities, a new switching law is presented to describe the dynamic behavior of the network topology, where the deterministic switching signal regulates the proper feedback sojourn probability information. Using historically transmitted packets, a novel memory-based DETP in the discrete-time domain was built simultaneously to alleviate energy consumption and improve system performance. On the basis of Lyapunov theory, the augmented dynamics are stochastically stable in the presence of a preset performance level. To this end, two simulation examples were presented to verify the validity and applicability of the developed methodology.

This article concerns the stability issues of neural networks with time-varying delays. The purpose is to establish less conservative stability conditions for delayed neural networks. In order to achieve this goal, a time-varying Lyapunov functional method is proposed for stability analysis of delayed neural networks. The main feature of this method is to construct different Lyapunov functionals in different time-varying delay subintervals, which relaxes the restriction requirement of traditional Lyapunov functionals constructing a common Lyapunov functional in the whole delay interval. Based on the developed time-varying Lyapunov functional, some new stability conditions for delayed neural networks are obtained. Finally, two examples are given to verify the effectiveness of the derived stability conditions.

In this chapter, the synchronization problem between two coupled hyperbolic neural networks (CHNNs) with delays is studied. A simple but effective impulsive pinning controller strategy is designed to achieve exponentially outer synchronization on two CHNNs. To get rid of strict restrictions on delays, the Lyapunov functional approach, the comparison principle and the average impulsive interval method are used to investigate the outer synchronization problem, and a novel criterion is obtained. Furthermore, an example is presented to show our results are new and satisfactory.

This article presents a novel command-filter-based finite-time fuzzy preassigned performance adaptive control approach for nonlinear systems with exogenous disturbances and asymmetric time-varying constraints. Combining the asymmetric barrier Lyapunov function (ABLF) with preassigned performance control (PPC), it is ensured that the state$x_{1}$satisfies the required asymmetric time-varying state constraints (ATSCs), and the system output y can track the desired signal with achieving preassigned performance indices (PPIs). The command-filter-based control design not only circumvents the computing complexity in the backstepping but also reduces the conservativeness of the assumption regarding the desired signal and the time-varying asymmetric constraints. Additionally, the nonlinear disturbance observer method (NDOM) is employed to effectively estimate unknown exogenous disturbances and enhance the robustness of the closed-loop system. Through rigorous theoretical analysis, the proposed finite-time command filter-based control method effectively ensures that all variables are bounded within a finite time. The practicality and feasibility of the proposed approach are further validated by simulation results.

This article is concerned with the observer-based asynchronous control for discrete hidden semi-Markov switching power systems under random denial-of-service (DoS) attacks. Considering the mismatched behavior between the controller and the system, the designed controller based on the observer model runs asynchronously with the system. The hidden stochastic switching model is introduced to characterize this mismatched behavior. Due to the difficulty in obtaining complete information about the semi-Markov kernel (SMK) in practice, the elements in the SMK of the underlying system associated with the hidden mode are considered to be incompletely known. Next, regarding the random DoS attacks and incomplete SMK, the conditions on the existence of the asynchronous controller based on the observer model are proposed by employing the stochastic Lyapunov function, and the closed-loop system is guaranteed to be mean-square stable. Finally, the effectiveness of the proposed scheme is validated through an example.

In this article, we address the quasi-bipartite consensus problem concerning a type of time-varying multiagent systems (MASs), which are subjected to energy harvesting protocols and random occurring faults. The creative aspects of this study can be emphasized as follows: first, the dynamics of the considered system are time-varying and stochastic, which is in accordance with the practical application closely. Meanwhile, a differential privacy protection mechanism is proposed, which realizes the goal of sensitive information protection via adding random noises following the Laplace probability distribution, and an energy collecting procedure is proposed to gather external energy for information exchange. Furthermore, the ultimate control objective is to implement an appropriate quasi-bipartite consensus controller to ensure that the probability of the state/output error residing within an allowable range exceed a fixed scalar. By means of recursive linear matrix inequalities (RLMIs) and the matrix analysis theory, sufficient conditions for guaranteeing quasi-bipartite consensus are derived, and an optimal constraint set is obtained by addressing a convex minimization problem. Finally, the availability of the employed approach are upheld by two illustrative examples.

This work deals with the finite-time control issue for a kind of semi-Markov switching systems in the presence of singular perturbation and general transition rates. The mode switching behaviors are determined by a semi-Markov process (SMP), and the variations of the SMP are revealed by a deterministic switching signal. Furthermore, by analyzing conservatism, a generalized framework of uncertain transition rates is established. For the sake of decreasing triggering rates and maintaining control performance, on the basis of the quantization range of the dynamic quantizer, a novel triggering condition is efficiently constructed. As such, sufficient conditions are attained to maintain the prescribed performance within the finite-time interval. Eventually, two numerical simulations are addressed to reflect the solvability and practicability of the achieved control strategy.

Under the framework of multiagent systems (MASs), this article is focused on the leaderless consensus of multiple Euler–Lagrange (EL) systems subject to denial-of-service (DoS) attacks. First, a new joint control protocol, which combines the distributed sampled-data (SD) control and adaptive distributed control (DC), is designed. The distributed SD control is equipped with logic processors, which can capture key information of DoS attacks, and the adaptive DC is conducive to relaxing some generally required constraint conditions. Second, a new property of a non-negative differentiable function is proposed, which is very helpful in solving the distributed SD control issue of multiple EL systems. Third, by setting up a$\textbf{W}$-dependent Lyapunov–Krasovskii functional (LKF) and utilizing the property of the non-negative differentiable function, new leaderless consensus results are derived for multiple EL systems with DoS attacks. The obtained leaderless consensus results are in the shape of linear matrix inequalities (LMIs) and can efficiently counter the influence of the DoS attacks. Ultimately, the effectiveness of the derived results is inspected by an MAS with multiple two-linked robot manipulators.

Roadside Unit (RSU) plays a significant role in Vehicle-to-Everything (V2X) service to accomplish adequate performance for the autonomous driving system. Haphazard RSU deployment and selection cause to diminish the frame-work efficiency and fail to meet the service capability of most vehicles. Reducing latency by timely delivering service is a phenomenal concern of edge orchestration to enhance the quality of service (QoS) with high reliability. With this motivation, this study focuses on maintaining a trade-off between RSU deployment and selection to achieve a high convergence rate of service offloading policy for effective service consolidation. An RSU-Assisted Service Consolidation (RASC) approach is proposed considering probabilities of three service demand rates, Computation-intensive service rates, and resource prediction rates based on Deep Reinforcement Learning. A resource-weight factor is designed to classify and map the arrived services to the potential RSU based on a discrete concave-convex procedure to avoid the execution hiccups for achieving a high convergence rate of service offloading policy. The average probability rate helps to select the potential RSU to fulfill the requirements of delay-sensitive service requests. The simulation results shows the RASC method improves the service reliability rate by at least 69.5%, the energy preservation rate by 27.1%, and the service offloading reduction rate by 73.08% compared to state-of-art schemes.

Role of RSUs become essential in enhancing the service reliability rate of the end vehicle to strengthen autonomous vehicle technology. Computation-intensive services are delay-sensitive, and most existingmethodsattempted comprehensively to meet the application deadline but have not reached the expectations due to classical computing. In this regard, we design a novel offloading decision-making method based on quantum theory through reinforcement learning. Grover's algorithm is employed to select a feasible device based on cost and energy usage probability ratio. Theoretical and mathematical validations and simulation outcomes confine the impact of novel decision-making methods on the statistical constraints of the heterogeneous framework.

This article aims to investigate the security consensus and composite anti-disturbance problems for a class of nonlinear multi-agent systems subjected to stochastic false data injection attacks (FDIAs) and multiple disturbances under a directed communication topology. To attenuate and reject of the negative effects of two types of disturbances, a disturbance observer (DO) is designed to counteract the disturbance produced by exogenous system, and the$\mathcal {H}_\infty $control method is adopted to attenuate the bounded errors and variables caused by the other type of disturbances and FDIAs simultaneously. To ensure the consensus performance of MASs, an observer-based control strategy is designed, and a novel adaptive compensation technique is proposed to not only evaluate the upper bounds of the unknown but bounded disturbances but also improve the accuracy of the state observer. Furthermore, a novel event-triggered mechanism (ETM) without requiring continuous communication among neighboring agents is developed to reduce the controller update frequency and the communication burden. Meanwhile, Zeno behavior is excluded. Finally, numerical simulations are provided to verify the availability of the designed method. Note to Practitioners—In multi-agent systems, network security is very important. For example, in smart power grid systems, it is necessary to use the method of state estimation to observe the system to guarantee its safe operation. However, the measured value of the instrument may be affected by FDIAs in the transmission process, thus changing the result of state estimation and causing misjudgment of the system. Similarly, in multi-vehicle systems, FDIAs may destroy the location information of vehicles and cause serious accidents. In addition, the system will be subjected to different types of disturbances in practice, thus reducing the performance of the system. In view of the threat of FDIAs and disturbances to the MAS, a composite anti-disturbance method and an observer-based control strategy are proposed. Meanwhile, to avoid the limitation of communication bandwidth in reality, a novel ETM is developed to save network resources.

This paper investigates the stability analysis and controller design issues for aperiodic sampled-data networked control systems with time-varying delays. Three main results are obtained. Firstly, the discretized model with delay is constructed as an feedback interconnected system formed by a structural uncertainty operator and a linear time-invariant system. Secondly, an exponential decay rate is introduced as a performance index of the system through model transformation, an equivalent stability condition for the system is also obtained, further, the conclusion is extended to systems with arbitrary initial states. On this basis, the exponential stability conditions with minimal computational complexity are given under an integral quadratic constraint. Thirdly, an iterative algorithm for the solution of the controller is given to deal with the effects of non-linear coupling terms. Finally, two numerical examples and an application example of an interleaved flyback modular integrated converter are used to illustrate the effectiveness and computational simplicity of the stability criterion and the controller design algorithm of this paper.

The proportion of distributed resources connected to the distribution network is gradually increasing. But in most scenarios, resources operate in passive response mode and cannot give full play to their active regulation potential. To awaken the regulation capability of distributed resources for active support of system frequency stability, a transmission and decision-making co-designed architecture is studied in this paper when a local supply-demand imbalance in the distribution network causes frequency instability. The architecture contains “data module”, “transmission module”, and “decision-making module”. Firstly, in the “data module”, based on the theory of multi-extreme learning machines and power flow calculation, the generation prediction of energy sources is performed to provide the data basis for generating regulation commands. Secondly, in the “transmission module”, through the nodal current equation-based sliding mode control and fast path reconstruction, the response strategy of the communication disturbance problem is proposed to provide the transmission support for generating regulation commands. Finally, based on the theory of “multiple factors matching” and “source-load interaction”, the source and load-side regulation commands are generated in the “decision-making module” by combining the transmitted data. Related case studies are carried out to verify the effectiveness of the proposed strategies.

In the multi-microgrid system, once a microgrid is severely disturbed into the alert or emergency state, the effective multi-mode management method is necessary to make the system restore the balance of supply and demand rapidly. Therefore, a hierarchical multi-mode management strategy is proposed in this study, which includes three steps:1. The first step is to predict and fit the support capacity of the neighbor microgrids and to determine whether these microgrids need to participate in the support, which constitutes the upper layer. To this end, an event-triggered mode management strategy is proposed considering the multi-source fitting and line loss factor, etc. 2. When the support of neighbor microgrids is not required, the second step is to manage local source-storage-load to restore the balance of supply and demand rapidly, which constitutes the lower layer. To this end, another event-triggered management strategy is designed to generate management commands without pre-processing data; 3. To establish the mathematical models of a microgrid in hybrid mode consisting of continuous operation status and discrete management commands, the related small signal model is designed as the third step, which can analyze the system stability conveniently. Finally, the effectiveness of methods is verified by case studies.

In this study, the output-feedback control (OFC) strategy design problem is explored for a type of Takagi-Sugeno fuzzy singular perturbed system. To alleviate the communication load and improve the reliability of signal transmission, a novel stochastic communication protocol (SCP) is proposed. In particular, the SCP is scheduled based on a nonhomogeneous Markov chain, where the time-varying transition probability matrix is characterized by a polytope-structure-based set. Different from the existing homogeneous Markov SCP, a nonhomogeneous Markov SCP depicts the data transmission in a more reasonable manner. To detect the actual network mode, a hidden Markov process observer is addressed. By virtue of the hidden Markov model with partly unidentified detection probabilities, an asynchronous OFC law is formulated. By establishing a novel Lyapunov-Krasovskii functional with a singular perturbation parameter and a nonhomogeneous Markov process, a sufficient condition is exploited to guarantee the stochastic stability of the resulting system, and the solution for the asynchronous controller is portrayed. Eventually, the validity of the attained methodology is expressed through a practical example.

Synchronization of complex networks with nonlinear couplings and distributed time-varying delays is investigated in this article. Since the mismatched parameters of individual systems, a kind of leader-following quasisynchronization issues is analyzed via impulsive control. To acquire appropriate impulsive intervals, the dynamic self-triggered impulsive controller is devoted to predicting the available instants of impulsive inputs. The proposed controller ensures the control effects while reducing the control costs. In addition, the updating laws of the dynamic parameter is settled in consideration of error bounds to adapt to the quasisynchronization. With the utilization of the Lyapunov stability theorem, comparison method, and the definition of average impulsive interval, sufficient conditions for realizing the synchronization within a specific bound are derived. Moreover, with the definition of average impulsive gain, the parameter variation scheme is extended from the fixed impulsive effects case to the time-varying impulsive effects case. Finally, three numerical examples are given to show the effectiveness and the superiority of proposed mathematical deduction.

In this article, a networked fault detection (FD) problem is investigated for interval type-2 T–S fuzzy systems. A novel adaptive memory-event-triggered mechanism (METM) is proposed by introducing historical information of the measured output in a prescribed sliding window. The current measured output in the traditional event-triggered mechanism is replaced by a weighting function-based historical information. As a result, the data releasing rate can be effectively reduced and maltriggering events aroused by unknown abrupt disturbance or measurement noise can be avoided as well. Meanwhile, an adaptive threshold depending on the historical information is utilized to further adjust the data releasing rate. The FD filter is designed and derived in terms of linear matrix inequalities to guarantee the$H_{\infty }$performance of fault detected systems. Finally, a hardware-in-loop simulation experiment platform is built to manifest the effectiveness of the proposed METM-based FD method.

This article mainly studies the projective quasisynchronization for an array of nonlinear heterogeneous-coupled neural networks with mixed time-varying delays and a cluster-tree topology structure. For the sake of the mismatched parameters and the mutual influence among distinct clusters, the exponential and global quasisynchronization within a prescribed error bound instead of complete synchronization for the coupled neural networks with clustering trees is investigated. A kind of pinning impulsive controllers is designed, which will be imposed on the selected neural networks with some largest norms of error states at each impulsive instant in different clusters. By employing the concept of the average impulsive interval, the matrix measure method, and the Lyapunov stability theorem, sufficient conditions for the realization of the cluster projective quasisynchronization are derived. Meanwhile, in terms of the formula of variation of parameters and the comparison principle for the impulsive systems with mixed time-varying delays, the convergence rate and the synchronization error bound are precisely estimated. Furthermore, the synchronization error bound is efficiently optimized based on different functions of the impulsive effects. Finally, a numerical experiment is given to prove the results of theoretical analysis.

In this article, we consider the input-to-state stability (ISS) problem for a class of time-delay systems with intermittent large delays, which may cause the invalidation of traditional delay-dependent stability criteria. The topic of this article features that it proposes a novel kind of stability criterion for time-delay systems, which is delay dependent if the time delay is smaller than a prescribed allowable size. While if the time delay is larger than the allowable size, the ISS can be preserved as well provided that the large-delay periods satisfy the kind of duration condition. Different from existing results on similar topics, we present the main result based on a unified Lyapunov-Krasovskii function (LKF). In this way, the frequency restriction can be removed and the analysis complexity can be simplified. A numerical example is provided to verify the proposed results.

In this article, we consider the load frequency control problem for a class of power systems based on the dynamic event-triggered control (ETC) approach. The transmission networks are unreliable in the sense that malicious denial-of-service (DoS) attacks may arise in the power system. First, a model-based feedback controller is designed, which utilizes estimated states, and thus can compensate the error between plant states and the feedback data. Then, a dynamic event-triggered mechanism (DETM) is proposed by introducing an internal dynamic variable and a timer variable with jump dynamics. The proposed (DETM) can exclude Zeno behavior by regularizing a prescribed strictly positive triggering interval. Incorporated in the ETC scheme, a novel hybrid model is established to describe the flow and jump dynamics of the power system in the presence of DoS attacks. Based on the hybrid dynamic ETC scheme, the power system stability can be preserved if the attacks frequency and duration sustain within an explicit range. In addition, the explicit range is further maximized based on the measurement trigger-resetting property. Finally, a numerical example is presented to show the effectiveness of our results.

In this article, the output-feedback tracking control problem is considered for a class of nonlinear time-delay systems in a strict-feedback form. Based on a state observer with reduced order, a novel output-feedback control scheme is proposed using the backstepping approach, which is able to guarantee the system transient and steady-state performance within a prescribed region. Different from existing works on prescribed performance control (PPC), the present method can relax the restriction that the initial value must be given within a predefined region, say, PPC semiglobally. In the case that the upper bound functions for nonlinear time-delay functions are unknown, based on the approximate capacity of fuzzy-logic systems, an adaptive fuzzy approximation control strategy is proposed. When the upper bound functions are known in prior, or in a product form with unknown parameters and known functions, an output-feedback tracking controller is designed, under which the closed-loop signals are globally ultimately uniformly bounded, and tracking control with global prescribed performance can be achieved. Simulation results are given to substantiate our method.

This article addresses the adaptive fuzzy control problem for switched nonlinear systems with state constraints. The unified barrier function (UBF) is introduced to solve the time-varying state constraints, which removes the feasibility conditions. By integrating command filter into backstepping control to avoid the "explosion of complexity." In addition, a novel event-triggered strategy is designed to deal with the asynchronous switching between subsystems and controllers without limiting the maximum asynchronous time, and mitigate the communication burden. Also, a new threshold function is introduced to overcome the difficulty of discontinuous triggering error at the switching instants. Then, by combining the improved admissible edge-dependent average dwell-time (AED-ADT) method with Lyapunov stability analysis, it is proved that all system signals are bounded and do not violate the predefined constraints under given switching rule. Finally, the numerical simulation results verify the superiority of the proposed algorithm, and the algorithm is applied to a ship maneuvering system.

This study is devoted to event-triggered fuzzy load frequency control (LFC) for wind power systems (WPSs) with measurement outliers and transmission delays. Due to the integration of wind turbine (WT) with nonlinearity, the T–S fuzzy model of WPS is established for stability analysis and controller design. To mitigate the network burden, a new sampled memory-event-triggered mechanism (SMETM) related to historical system information is presented. It has the following two merits: 1) the utilization of continuous memory outputs over a given interval is useful to reduce the information loss in the period of samples and the redundant triggering events induced by disturbances and noises and 2) an extra upper constraint is added in the triggering condition to generate a new event only when the error signal belongs to a bounded range, thus, the false events caused by measurement outliers can be differentiated out and then be dropped. By representing the memory signal with transmission delay as a time-varying distributed delay term, a T–S fuzzy time-varying distributed delay system is built up to model the$H_{\infty}$LFC WPS. With the help of the Lyapunov method and the integral inequality relying on distributed delay, some criteria are derived to solve the triggering matrix and fuzzy controllers. Finally, the merits of the proposed SMETM are tested by simulation results.

In this article, we consider multiparty synchronization (MS) for coupled memristive neural networks (CMNNs) with a time delay. Some Takagi–Sugeno typeif–then fuzzy rules are also introduced into the CMNNs. An event-triggered controller (ETC) is designed to achieve the MS in finite time, which avoids continuous control signals. Along with Lyapunov theory, differential inclusion theory, and inequalities, some criteria can be obtained to achieve the finite-time MS (FTMS), and the setting time (ST) of the FTMS can be calculated. By jointly considering ST, control inputs, error networks, and synchronization conditions, an optimization model is provided to get an optimal ETC (OETC). Further, the particle swarm optimization algorithm is utilized to solve the optimization model. Thus, it gives a method to choose control gains and parameters of an event-triggered function. Finally, two examples are given to verify the theoretical results. Especially, two comparative experiments are proposed to demonstrate that the OETC can save more control energy and reduce the number of triggered times.

This article focuses on a new outlier-resistant nonfragile control issue for a class of Takagi–Sugeno fuzzy delayed neural networks with reaction–diffusion terms. Compared with the existing delayed neural networks, fuzzy control rules and reaction–diffusion phenomenon are considered simultaneously, which makes the proposed models more practical. Furthermore, when subjected to abnormal interference, measurement outputs result in measurement outliers. In order to mitigate the negative effects on the estimation error, a state estimator scheme is presented by introducing a saturation function. By using an appropriate Lyapunov–Krasovskii functional and with the help of a free-weighting matrix, sufficient conditions can be deduced to guarantee the asymptotical stability and prescribed $\mathcal {H}_{\infty }$ performance index of the disturbance attenuation of the estimation error. Next, a design strategy of an outlier-resistant nonfragile state estimator is put forward by employing some decoupling techniques. An illustrative example is exploited to illustrate the validity and feasibility of the proposed state estimator. Finally, the obtained theoretical results are applied to image encryption. The experimental analysis demonstrates that the presented encryption scheme is feasible and effective.

The exponential stabilization of nonlinear distributed parameter systems (DPSs) with time-varying delay is studied in this article. With the employment of the Takagi–Sugeno (T–S) fuzzy model, the nonlinear DPSs are described as a fuzzy parabolic partial differential equation (PDE). Then, with the consideration of the measurement output and control input information quantized by a kind of dynamic quantizers, fuzzy static, and dynamic output feedback controllers are proposed. The time-delayed feedback controllers are also provided in the forms of static and dynamic. On the basis of the derived T–S fuzzy PDE model, Lyapunov-based control design strategies are proposed with respect to the effect of quantization. Moreover, by constructing appropriate Lyapunov functionals, sufficient conditions for designing the output feedback control gains and the quantizers' adjusting parameters, which guarantee the exponential stability of the fuzzy closed-loop system are provided in terms of standard linear matrix inequalities. Finally, we explore the output feedback controllers into the FitzHugh–Nagumo equation and Fisher equation as applications. The efficiency of the design strategies is indicated through the simulation results.

Addressing for the consensus control of multi-agent system (MAS) under the conditions that the directed communication topologies are switching and system nonlinearities are completely unknown, a global consensus control with fully distributed manner is proposed combining with fuzzy logic systems (FLS). FLS are used to approximate the unknown disturbances to enhance system robustness. To deal with the switching topologies of MAS, a reconstructing mechanism using a novel piecewise differentiable function is firstly proposed for the state and consensus errors of agents, which renders the state and consensus errors zero at each switching time instant and facilitates to the control design based on barrier functions, against the unexpected controller action at switching time instant. Incorporating with this reconstructing mechanism, a novel distributed controllers is designed, which is featured with low-complexity structure, fully distributed manner and adaptively reconstructing ability. These technical attempts contribute to some new results. Firstly, the global consensus of MAS with switching directed topologies and unknown nonlinearities is firstly achieved. Secondly, the consensus errors can be explicitly regulated to a prespecified arbitrary small residual set in the sense that the range of the set is the predefined functions given by designer. Finally, simulation results demonstrate the claims.

This article investigates the fault detection problem of unmanned marine vehicles (UMVs) under the influence caused by replay attacks. First, the dynamics of UMV are modeled by a Takagi--Sugeno (T--S) fuzzy system with an unknown membership function, which includes the nonlinear coupling of the internal state of the system, the environmental multisource disturbance as well as the potential thruster failure on UMV. Then, the possible replay attack from the sensor to the shore-based center is considered, and a switching-type attack tolerant fault detection filter is designed. Sufficient conditions are given to ensure that the filtering augmented system is stable and with stochastic finite frequency$H_{\infty }$and$H_{-}$performances, which reflect the robustness to the disturbance and sensitivity to the fault. On this basis, through a series of mathematical processing, the linear solvable conditions for the design of fault detection filters are obtained. Finally, the effectiveness of the proposed algorithm is verified by simulations.

This article studies a nonlinear disturbance observer (NDO)-based finite-time fuzzy adaptive dynamic surface control (DSC) of nonlinear systems (NSs) with external disturbances and preassigned performance indices. By constructing a type of finite-time preassigned-performance function (FTPF), the tracking error variable is confined within the boundaries of the FTPF such that the performance metrics, for instance, steady-state error, and convergence time, could be satisfied. Incorporating fuzzy adaptive control with the NDO technique, an effective composite NDO (CNDO) scheme is built to reckon the unknown composite disturbances, including unknown external disturbances and fuzzy approximation errors, which implies that fuzzy approaching errors can be further cut down. It is confirmed that the closed-loop system is semiglobally practically finite-time stable, as well as the tracking error and convergence time satisfy the predefined performance indices. In the end, the validity of the CNDO-based finite-time adaptive DSC scheme has been evidenced by a practical example model.

The sliding mode control (SMC) problem is studied for interval type-2 fuzzy semi-Markovian jumping systems (S-MJSs) subject to channel fading. To reduce the network burden, a dynamic event-triggered protocol is adopted to improve the transmission efficiency. A key feature is that the signal transmission is inevitably affected by fading phenomenon due to random noise and amplitude attenuation during the wireless communication. The main challenge lies in designing an appropriate fuzzy control scheme to achieve the reachability of the specified sliding region in line with channel fading. Under the common sliding surface, a fuzzy SMC law is constructed, which is related to the state signals affected by dynamic event-triggered protocol and channel fading. Then, by means of the boundary information of the global membership functions, sufficient conditions to ensure the stochastic stability of the underlying system, and the system states can be driven on the specified sliding region within a finite-time interval, which attenuates the influence of the channel fading. In the end, the tunnel diode circuit model is simulated to verify the proposed SMC strategy.

The stability of Takagi–Sugeno (T–S) fuzzy systems with time-varying delay is investigated in this article. First of all, a novel Lyapunov–Krasovskii functional (LKF) is proposed by fully utilizing single integral polynomial-delay-product terms and membership-function-dependent matrices, where more delay information is considered. Second, by introducing negative integral estimation inequalities and polynomial inequality, the estimation gap of derivatives is further decreased. As consequence, the criterion with less conservatism is presented. Finally, the examples are utilized for verifying the validity of the stability approach.

A fuzzy-model-based approach is developed to investigate the reinforcement learning-based optimization for nonlinear Markov jump singularly perturbed systems. As the first attempt, an offline parallel iteration learning algorithm is presented to solve the coupled algebraic Riccati equations with singular perturbation and jumping parameters. Furthermore, based on the integral reinforcement learning approach, a novel online parallel learning algorithm is proposed by employing the slow and fast sampled data simultaneously, where the impacts of stochastic jumping and ill-conditioned numerical problems are avoided. Meanwhile, the convergence of the proposed learning algorithms is proved. Finally, we present a tunnel diode circuit model to demonstrate the efficacy of the proposed methods.

This article investigates the exponential synchronization of fuzzy coupled reaction-diffusion neural networks (RDNNs) under hybrid random cyberattacks. To efficaciously tolerate the cyberattacks and guarantee the expected performance for the proposed systems, a fuzzy-regulation-dependent adaptive spatiotemporal security sampled-data-based event-triggered control scheme (SDBETCS) is first introduced according to distinct fuzzy regulations. In light of the current and latest sampling signals, the threshold parameters can be timely and flexibly updated and the associated adaptive spatiotemporal SDBETCSs can be adaptively regulated for different fuzzy rules. In comparison with the conventional fuzzy SDBETCSs, the designed fuzzy adaptive spatiotemporal SDBETCS can not only reduce the event-triggering frequency but also effectively conserve more finite network communication resources. Through considering a discontinuous Lyapunov functional, a new exponential synchronization criterion is provided for fuzzy coupled RDNNs. Furthermore, a more general fuzzy adaptive spatiotemporal SDBETCS with time-dependent and continuous threshold function is presented to compare with the traditional fuzzy SDBETCS. Finally, demonstrative examples are given to verify the validity and feasibility of the theoretical analysis results and illustrate its potential application in image secure communication.

In this article, we study the design of fuzzy synchronization controller of Takagi–Sugeno (T–S) fuzzy neural networks with distributed time-varying delay and probabilistic network communication delay. Compared with the existing model of neural networks with distributed time-varying delay without kernel, a more general model containing a distributed delay kernel is considered. By utilizing the probability distribution of random communication delays, a distributed but deterministic delay model is established. In this model, the delay probability density function is treated as the distributed delay kernel. By developing a new Lyapunov–Krasovskii functional related to the distributed delay kernels and using an integral inequality, new sufficient conditions for the existence of a synchronization controller are presented. Finally, a numerical simulation and an application of encrypting the image are carried out to illustrate the effectiveness of the developed strategy.

To reduce the computational burden and resist the denial-of-service (DoS) attacks, a resilient distributed sampled-data control scheme is proposed for multiagent systems. The agent states are sampled periodically by the sensors. DoS attacks disrupt the data communication from transmitters to controllers randomly or periodically with a limited duration time. Information on DoS attacks can be obtained by introducing novel logic processors embedded in corresponding controllers. Next, the problem of resilient control can be converted into one concerned with the upper and lower bound of the sampling interval of an aperiodic sampled-data control system. Some sufficient criteria for developing resilient distributed controllers are derived using the novel looped Lyapunov functional approach and the free-matrix-based inequality method. Finally, two illustrative examples, unmanned aerial vehicles and the two-mass-spring systems, are provided to demonstrate the efficiency of the proposed resilient distributed sampled-data control protocols against the DoS attacks.

In this paper, the string stable platoon control problem of discrete-time networked vehicle systems is considered by using distributed model predictive control (MPC) based method. An optimization problem is established to minimize the cost function associated to the system trajectories. The last-step shifting method is applied to set the local optimal solution as the assumed solution and send it to the neighbor vehicles. By using the sum of the cost function as Lyapunov function, the stability of the closed-loop platoon system is studied. Comparing with existing results, the string stability, which is the unique characteristics of the platoon system, is guaranteed under the bidirectional-based structure as well as the predecessor-follower-based information flow structure. Finally, several simulations are presented to demonstrate the effectiveness of the proposed algorithms.

This article studies the memristive neural networks with multiple time delays (MNNsMTDs). The topology of networks is signed, which contains both cooperative and competitive relationships. Two controllers without time delays are designed to achieve finite-time bipartite synchronization (FTBS) and practical FTBS (PFTBS) of MNNsMTDs. A novel controller with a saturation function rather than a sign function is proposed to avoid chattering. Along with the Lyapunov function method, some mathematical techniques, and scaling inequalities, some sufficient conditions for FTBS and PFTBS of MNNsMTDs are attained. Besides, this article also concerns fixed-time bipartite synchronization (FXBS) and practical FXBS (PFXBS) of MNNsMTDs. An optimization model is designed to obtain some optimal control parameters. An algorithm based on particle swarm optimization (PSO) is provided to solve this model. Some numerical examples are included to demonstrate the correctness and applicability of the approaches.

This article is devoted to the output feedback control of nonlinear system subject to unknown control directions, unknown Bouc-Wen hysteresis and unknown disturbances. During the control design process, the design obstacles caused by unknown control directions and Bouc-Wen hysteresis are eliminated by introducing linear state transformation and a new coordinate transformation, which avoids using the Nussbaum function with high-frequency oscillation to deal with the issue. Besides, to settle the issue caused by the unknown disturbances, a novel nonlinear disturbance observer is designed, which has the characteristics of simple structure, low coupling, and easy implementation. Especially, a compensation item is constructed to offset the redundant items generated in the backstepping design process. Simultaneously, using the neural network and backstepping technology, an output feedback controller is devised. The controller ensures that all closed-loop signals are bounded, and the system output, state observation error, and disturbance observation error converge to a small neighborhood of the origin. Finally, to illustrate the effectiveness of the proposed scheme, simulation verification is carried out based on a numerical example and a Nomoto ship model.

This brief is concerned with iterative learning control (ILC) of constrained multi-input multi-output (MIMO) nonlinear systems under the state alignment condition with varying trial lengths. A modified reference trajectory is constructed to meet the alignment condition by adjusting the reference trajectory to be spatially closed. Resorting to the barrier composite energy function (BCEF) approach, an adaptive ILC scheme is built to guarantee the bounded convergence of the resultant closed-loop system. Illustrative examples are presented to verify the validity of the proposed iteration scheme.

bipartite synchronization issue is studied for a class of discrete-time coupled switched neural networks with antagonistic interactions via a distributed dynamic event-triggered control scheme. Essentially different from most current literature, the topology switching of the investigated signed graph is governed by a double-layer switching signal, which integrates a flexible deterministic switching regularity, the persistent dwell-time switching, into a Markov chain to represent the variation of transition probability. Considering the coexistence of cooperative and antagonistic interactions among nodes, the bipartite synchronization of which the dynamics of nodes converge to values with the same modulus but the opposite signs is explored. A distributed control strategy based on the dynamic event-triggered mechanism is utilized to achieve this goal. Under this circumstance, the information update of the controller presents an aperiodic manner, and the frequency of data transmission can be reduced extensively. Thereafter, by constructing a novel Lyapunov function depending on both the switching signal and the internal dynamic nonnegative variable of the triggering mechanism, the exponential stability of bipartite synchronization error systems in the mean-square sense is analyzed. Finally, two simulation examples are provided to illustrate the effectiveness of the derived results.

This article focuses on designing an event-triggered impulsive fault-tolerant control strategy for the stabilization of memristor-based reaction-diffusion neural networks (RDNNs) with actuator faults. Different from the existing memristor-based RDNNs with fault-free environments, actuator faults are considered here. A hybrid event-triggered and impulsive (HETI) control scheme, which combines the advantages of event-triggered control and impulsive control, is newly proposed. The hybrid control scheme can effectively accommodate the actuator faults, save the limited communication resources, and achieve the desired system performance. Unlike the existing Lyapunov-Krasovskii functionals (LKFs) constructed on sampling intervals or required to be continuous, the introduced LKF here is directly constructed on event-triggered intervals and can be discontinuous. Based on the LKF and the HETI control scheme, new stabilization criteria are derived for memristor-based RDNNs. Finally, numerical simulations are presented to verify the effectiveness of the obtained results and the merits of the HETI control method.

This article focuses on the design of a mode- dependent adaptive event-triggered control (AETC) scheme for the stabilization of Markovian memristor-based reaction-diffusion neural networks (RDNNs). Different from the existing works with completely known transition probabilities, partly unknown transition probabilities (PUTPs) are considered here. The switching conditions and values of memristive connection weights are all correlated with Markovian jumping. A mode-dependent AETC scheme is newly proposed, in which different adaptive event-triggered mechanisms will be applied for different Markovian jumping modes and memristor switching modes. For each given mode, the corresponding event-triggered mechanism can efficiently reduce the number of transmission signals by adaptively adjusting the threshold. Thus, the mode-dependent AETC scheme can effectively save the limited network communication resources for the considered system. Based on the proposed control scheme, a new stabilization criterion is set up for Markovian memristor-based RDNNs with PUTPs. Meanwhile, a memristor-dependent AETC scheme is devised for memristor-based RDNNs. Finally, simulation results are presented to verify the effectiveness and superiority of the analysis results.

This article is dedicated to design asynchronous aperiodic sampled-data controller for a class of Markov jump systems with switching transition rate. The sampling controller corresponding switching transition rate matrix mode is not synchronized with the system corresponding switching transition rate matrix mode due to the possible switching of the transfer rate matrix in the sampling interval. With the help of T–S fuzzy models, the state-space representation of the system is turned into a fuzzy Itô stochastic switched Markov jump systems. By constructing novel Lyapunov functionals, mean square exponential stability criteria, and dissipativity criteria are presented. Furthermore, an asynchronous aperiodic sampled-data controller is designed for fuzzy Itô stochastic Markov jump systems. At last, by fuzzing the nonlinear tunnel diode circuit model and nonlinear mass-spring-damper mechanical model, the effectiveness and lower conservativeness of the proposed method are illustrated.

This article is concentrated on the load frequency control for interconnected multiarea power systems (IMAPSs) with nonhomogeneous sojourn probabilities (NSPs) and cyber-attacks. A generalized framework of NSPs is formulated to describe the dynamic behavior of IMAPSs. To govern variations of sojourn probabilities, a deterministic switching signal is introduced using the average dwell-time technique. Essentially, different from the existing protocols, an improved event-triggered protocol that is relevant to the dynamic quantizer parameter is presented, thereby increasing triggering intervals. Furthermore, both denial-of-service and deception attacks, which obey Bernoulli distributions, are considered during information transmission. By virtue of the Lyapunov theory, the mean-square exponential stability of the considered systems is established. Finally, the effectiveness of the obtained results is verified via a numerical example.

This article is concerned with the security$\mathcal {H}_{\infty }$fuzzy control problem for Takagi–Sugeno (T–S) fuzzy semi-Markov switching systems (SMSSs) with event-based strategy and hybrid cyber-attacks. To efficiently use network resources and avoid unnecessary signal transmission, a new mode-dependent dynamic event-triggered mechanism (ETM), whose threshold parameter changes with varying states and modes is developed for each subsystem of fuzzy SMSSs. A more practical network environment subject to deception and denial-of-service (DoS) attacks is considered, and two Bernoulli distributed variables are adopted to characterize the randomly occurring hybrid attacks in a unified framework. By the mode-dependent Lyapunov function, sufficient conditions are attained to assure that the closed-loop system is stochastically stable with an$\mathcal {H}_{\infty }$attenuation performance. Finally, the validity and superiority of the proposed theoretical approach is demonstrated via its application to the Duffing–Van der Pols oscillator.

In this article, the problem of adaptive nonsmooth state-feedback stabilization for uncertain output-constrained cascade switched systems with a general form, whose subsystems possess mixed powers (MPs) and zero dynamics (ZDs), is investigated. To solve the problem of adaptive control, on subsystems of switched systems, some mild assumptions, such as ZDs with input-to-state stability (ISS), nonlinear terms with a growth condition and some well-known small signal conditions, have been imposed. State-feedback control laws are designed and an adaptive law is constructed in a systematic way by revamping the so-called adding a power integrator (AAPI) technique, a special back-stepping-like method. In order to cope with output constraints (OCs), a common Lyapunov function, including a tangent-type barrier Lyapunov function term, is constructed. The method is proposed in a unified manner which can tackle adaptive stabilization of switched systems due to that it can deal with the cases of uncertain cascade switched systems with/without OCs, no need to change the structure of the controller. Simulation results are provided to illustrate the efficiency of the proposed method.

This article focuses on the event-triggered synchronization of delayed discrete-time chaotic neural networks with quantized effect and stochastic deception attack. First, for alleviating the network communication and communication burden, an event-triggered mechanism and a logarithmic quantizer are employed, separately. Second, for integrating the impact of event-triggered scheme, quantization, and cyberattack in a unified framework, a synchronization error model is introduced. Third, based on the Lyapunov–Krasvovskii functional (LKF), some sufficient conditions are established to guarantee the synchronization of drive system and response system. Furthermore, the co-design controller and homologous event-triggered parameters are also derived according to the presented asymptotic stability condition. Finally, the availability of the proposed method is verified by some numerical examples.

In this article, we investigate the consensus problem of multiagent systems (MASs) based on the event-triggered control (ETC) method. The communication between agents is transmitted through unreliable networks with denial-of-service (DoS) attacks. By equipping the MAS with estimation capabilities so as to estimate the lost measurements caused by DoS attacks, we present a hybrid dynamic event-triggered strategy (HDETS) for the consensus of MAS subject to unreliable networks. Based on the proposed HDETS, we can preserve the consensus of MASs provided that the DoS attacks satisfy a condition on its frequency and duration. In addition, it can guarantee the Zeno-freeness with a designed strictly positive minimum event-triggered interval as well. Furthermore, we revisit the estimator with the trigger-resetting property, and present a finite time resetting condition for the estimation error, which illustrates the maximal DoS attacks intensity that the MASs can tolerate. Finally, the developed results are verified by a spacecraft formation simulation example.

The$L$th order Rice model can effectively describe imperfect transmission, including amplitude attenuation, time delays, and stochastic disturbance, and is a popular channel fading model in one-dimensional (1-D) systems. However, due to the state evolution feature of two-dimensional (2-D) systems, the previous state signals of 2-D systems are not as directly determinable as those of 1-D systems such that the construction of the conventional and obvious$L$th order Rice model become difficulty for 2-D systems. Motivated by these, this work will investigate the sliding mode control (SMC) problem of the 2-D Fornasini–Marchesini (FMII) system under Rice fading channels. First, the previous$p$-step state signals are defined according to the evolution feature of the FMII model. Afterwards, a new applicable$L$th-order Rice fading model for 2-D FMII systems is constructed, which not only has 2-D structural characteristics but also has similar properties to 1-D cases. A 2-D-type sliding function involving the predefined previous$L$-step state signals and channel attenuation coefficients is designed, which exhibits a similar structural feature to the FMII model. A feasible SMC law is designed via the available fading states to achieve the ultimately uniformly boundedness in the mean square of the closed-loop system and the reachability of the sliding domain around the specified sliding surface. Furthermore, an optimization-solving algorithm is introduced to minimize the convergent bound. Finally, an example is applied to demonstrated the proposed results.

This article concentrates on the discrete-time sliding mode control (DTSMC) problem for uncertain networked semi-Markovian switching systems (S-MSSs) under random denial-of-service (DoS) attacks. The semi-Markovian kernel (SMK) approach is utilized such that the switching among different modes is mutually governed by the transition probability and the sojourn-time distribution function. Considering randomly occurring DoS attacks, a sliding mode function related to the attack probability is constructed to analyze the impact of malicious attacks. Then, sufficient conditions under the equivalent DTSMC law are derived in view of the$\sigma $-error mean square stability criterion. Furthermore, the synthesis problem of the proposed DTSMC law ensures that the resulting closed-loop system dynamics can be driven onto the prespecified sliding region within a limited time. Finally, an electronic throttle control system is shown to validate the proposed algorithm.

This article investigates the sliding mode control (SMC) for discrete-time nonlinear semi-Markov jump models with a partly known semi-Markov kernel (SMK). The nonlinear system is characterized by the Takagi–Sugeno (T–S) fuzzy model, where the membership functions for fuzzy rules are designed to be related to the system mode. In view of the fact that the statistical characteristic of the SMK is difficult to fully obtain in practical engineering, the SMK is recognized to be partly known with less conservativeness than both semi-Markov jump models with completely known SMK and Markov jump models with partly known transition probabilities. On the basis of classical Lyapunov stability and fuzzy-model-based approach, novel convex mean-square stability is proposed for the underlying system by eliminating the nonlinear coupling terms with the aid of additional matrix variables. Afterward, a fuzzy SMC law strategy is constructed to guarantee the reachability of the discrete quasi-sliding mode. Finally, a robot arm model is simulated to verify the proposed fuzzy SMC strategy.

This article is dedicated to data-driven control of networked nonlinear systems with event-triggered output. An improved extended state observer is constructed to estimate unknown disturbances. An output estimator is built on the triggered output and the estimated output. Consequently, triggering conditions for single-input single-output and multiple-inputs and multiple-outputs systems are individually proposed by integrating the estimated disturbances, the true and the estimated tracking errors. Sufficient conditions are established to guarantee that the resultant tracking error systems are uniformly ultimately bounded. The proposed strategies are verified by illustrative numerical examples.

This article investigates the resilient dynamic event-triggered control issue of interval type-2 T-S fuzzy systems (IT2 T-S FSs) under denial-of-service (DoS) and deception attacks. A novel resilient dynamic event-triggering mechanism (DETM) and the corresponding fuzzy dynamic event-triggered controller are proposed to solve the stochastically exponential stability (SES) problem of concerned IT2 T-S FSs in the presence of DoS and deception attacks. Meanwhile, an aperiodic-sampling-based piecewise loop Lyapunov functional is constructed. It is shown that the resulting closed-loop IT2 T-S FSs can achieve SES, and under the piecewise loop Lyapunov functional, some relaxed conditions are derived. In addition, the derived results can apply to IT2 T-S FSs subject to DoS attacks, static event-triggered control, or dynamic periodic-sampling-based event-triggered control. Finally, the effectiveness of the achieved results is verified by a numerical simulation on one-link manipulator.

This article is centered on the formulation of a refined adaptive sampled-data-based event-triggering control (ASDBETC) scheme for the synchronization of reaction–diffusion complex-valued neural networks (RDCVNNs) with probabilistic time-varying delays (TVDs). A refined ASDBETC mechanism is first proposed in a hierarchy structure, which differs from some conventional sampled-data-based event-triggering control mechanisms with preordained invariable threshold. The refined ASDBETC mechanism can adaptively adjust the orientation and frequency of event-triggered threshold parameters via the variational tendency of states and the corresponding Euclidean distance between states. Therefore, an intact bidirectional regulative mechanism that is sensitive to the changes of state is legitimately established to provide additional flexibility, which is conducive to better compromise between communication resources and control performance. Through considering the effect of uncertainties, the random TVDs belonging to two different intervals by a probabilistic form are introduced. Then, by leveraging a novel time-related Lyapunov–Krasovskii functional (LKF) that contains the more realistic sampled behaviors on the entire sampled interval, new synchronization conditions and controller design method are derived for RDCVNNs. Finally, the advantages of the refined ASDBETC strategy, the availability, and practicability of the developed theories are corroborated via two simulation examples.

In this article, a distributed-delay-dependent method is proposed to deal with the stabilization for interval type-2 Takagi–Sugeno fuzzy systems subject to stochastic network delay and actuator saturation. To deal with the stochastic feature of network-induced delays, their probability density function is utilized to establish the distributed delay model, which is more practical than the traditional time-varying network delay model. Meanwhile, in order to enlarge the estimation of the domain of attraction, a polytopic strategy composed of a state vector and a distributed-delay-dependent vector is proposed to treat the saturation nonlinearity. By constructing a distributed-delay-dependent Lyapunov–Krasovskii functional, new and less conservative conditions are achieved to guarantee the stability of the established closed-loop system. Additionally, two simulation examples are carried out to illustrate the advantages of the proposed strategy.

The synchronization in probability of reaction-diffusion neural networks (RDNNs) with stochastic sampling is studied in this article. By introducing a stochastic switching parameter, a new switching system protocol is proposed for stochastic sampling control systems. The switching system protocol effectively improves the existing methods. By the protocol, the stochastic switching sampled-data controller is designed, and the considered system is transformed into a switching system. Different from the existing sampled-data controllers with determined control gains, the stochastic switching sampled-data controller is with switching gains, which is more elastic. Then, by constructing a new stochastic switching Lyapunov–Krasovskii functional (LKF), using the law of large numbers and the Lagrange mean value theorem, new synchronization in probability criteria are established for RDNNs. In the mean time, the wanted stochastic switching sampled-data controller gains are obtained. Moreover, the synchronization in probability issue is also studied for NNs with stochastic sampling. Finally, the effectiveness of the proposed results are verified by two numerical examples.

The asymptotical synchronization in mean square of reaction–diffusion neural networks (RDNNs) with random delays is studied in this article. By sampling on both the time domain and spatial domain, a time–space sampled-data controller (TSSDC) is designed, which can efficiently save the network communication resources for RDNNs. A new processing method for the TSSDC is provided. Compared with the existing methods, the processing method here can capture more sampling information and is more concise. An extended Poincaré–Wirtinger inequality is proposed, which is in matrix form and less conservative. Then by constructing a sampling-dependent LKF, using the extended Poincaré–Wirtinger inequality and Hölder inequality, new mean square asymptotical synchronization criteria are set up for RDNNs with random delays, and the desired TSSDC gain is obtained. At length, a numerical example is given to verify the effectiveness and superiority of the obtained results.

This article tackles the distributed output-feedback containment control task for networked hypersonic flight vehicles (HFVs) in the presence of limited communication resources. Distinctive with the state-of-the-art control methodologies designed for single/multiple HFVs, the distinguishing features of our design lie in that: 1) a novel switching event-triggered information transmission mechanism embedded with an error-dependent monotonically decreasing power term is devised in such a way that the frequencies of data transmission among distinct HFVs can be significantly reduced, while at the same time maintaining desired consensus tracking errors; 2) a new set of error variables is delicately formulated such that the containment control task is realized in the sense that all follower HFVs are ensured to converge to a dynamic convex hull formed by leader HFVs; and 3) the priori knowledge of flight state variables is not required during the control design. To be precise, a high-gain observer is resorted to tackle the challenge that the attitude angles information is normally difficult to be obtained precisely in a practical hypersonic regime. Comparative simulation tests are finally carried out to validate the superiorities of the obtained theoretical findings.

This paper studies the stability and$\mathscr{L}_{1}$-gain control of positive systems with Markov switching parameters and impulses. Markov process is applied to describe the switching parameters and structures. The positivity constraint, Markov stochastic processes and impulsive jumps are taken into account in a unified framework. By utilizing the stochastic co-positive Lyapunuov function, some new conditions on stochastic stability and$\mathscr{L}_{1}$-gain characterization of positive impulsive Markov switching systems (PIMSSs) are acquired. The stochastic stability and$\mathscr{L}_{1}$-gain characterization of the closed-loop systems can be ensured via the$\mathscr{L}_{1}$-gain mode-dependent state-feedback controller. Moreover, an example is presented to exhibit the usefulness of the obtained results.

This paper concerns the finite-time (FT) proportional-integral-derivative (PID) predictive control for networked control systems (NCSs) with time-varying delays. A PID networked predictive control (PID-NPC) method is first proposed. This method makes use of historical information and its error of prediction state. Furthermore, an augmented switched control system is established in which the switching signal is determined by the time delay. Sufficient conditions are derived so that the resulting systems are finite-time boundedness (FTB). Then novel FT-PID controllers and the corresponding FT observers are given to compensate for the time-varying delays actively. Simulation example illustrates the effectiveness of the proposed method.

This work concerns the event-based asynchronous filtering problem for T-S fuzzy nonhomogeneous Markov switching systems with variable packet dropouts. The discrete-time nonhomogeneous Markov process is adopted to depict mode switching among subsystems, in which the time-varying transition probabilities are characterized by a polytope structure. The variable packet dropouts are developed to describe the randomly occurring packet dropouts, where the arriving rate remains variable and uncertain. Aiming to save the limited network bandwidth, event-triggered strategy and quantization scheme are presented. By establishing the fuzzy-rule-dependent Lyapunov functional and applying a hidden Markov model policy, sufficient criteria are gained and asynchronous filters are designed by solving linear matrix inequalities (LMIs). Finally, the applicability of the proposed filtering strategy is verified by an inverted pendulum model.

In this paper, the adaptive fuzzy control of switched stochastic nonlinear systems with set-time prescribed performance based on event-driven mechanism is studied. The creative part of this paper is that based on the set-time performance function, a modified event-triggered strategy that considers asynchronous switching to deteriorate system performance without strict assumptions is presented, which avoids Zeno behavior and saves communication resources. Then, by using backstepping recursive design technique, It$\hat {o}$’s differential lemma and mode-dependent average dwell time (MDADT) method, a novel adaptive performance control scheme is proposed, which can ensure that all the variables in the system are semiglobally uniformly ultimately bounded (SGUUB) in probability and the tracking error gets into prescribed boundary no later than an arbitrarily adjusted setting time. Finally, the proposed algorithm is applied to a RLC circuit and its practicability is verified via simulation results.

Nowadays, with the development of communication technology and its application in islanded microgrid, the pure power grid has gradually become a kind of cyber-physical system. In this system, the communication data and consensus algorithm are widely used in the secondary control of energy resources. However, in the communication process, there are also risks of network attacks, such as denial of service attack. To deal with this kind of attack existing in the data uploading channel during secondary control, the evolutionary game-based defense mechanism is designed, and the main works are as follows: firstly, the caused influence by attack on the control effect is analyzed through constructing a small-signal model. Secondly, two defense strategies including “Adjacent prediction” and “Path reconstruction” are designed. Facing different attack situations, the most suitable strategy can be selected via the evolutionary game. Thirdly, a game-based active defense strategy is designed, which can simulate the attack probability in the near future so that the defense can be prepared in advance. Finally, based on Laplace transformation and$\text{H}_{\infty } $robust theories, the parameter design in defense strategies is completed. The effectiveness of the above strategies is shown in multiple case studies.

This article is concerned with the problem of finite-time$\mathcal {H}_{\infty }$state estimation for switched genetic regulatory networks with randomly occurring uncertainties. The persistent dwell-time switching rule, as a more versatile class of switching rules, is considered in this paper. Besides, several random variables that obey the Bernoulli distribution are used to represent randomly occurring uncertainties. The overriding purpose of this article is to design an estimator to ensure that the estimation error system is stochastically finite-time bounded and satisfies the$\mathcal {H}_{\infty }$performance. Based on this, sufficient conditions for the explicit form of the estimator gains can be obtained by the Lyapunov method. Finally, a numerical example is given to verify the correctness and feasibility of the proposed method.

This article concentrates on the synchronization of discrete-time persistent dwell-time (PDT) switched bidirectional associative memory inertial neural networks with time-varying delays. Through the use of the switched system theory related to the PDT, the convex optimization technique together with some straightforward decoupling methods, an appropriate mode-dependent controller with nonfragility is developed to acclimatize itself to some practical circumstances. Simultaneously, sufficient conditions of ensuring the$\mathcal {H}_{\infty }$performance and exponential stability for the resulting switched synchronization error system are derived. Finally, a numerical example is utilized to show the validity of the model constructed and the influence of the PDT on the$\mathcal {H}_{\infty }$performance. In addition, an image encryption example is employed to show the potential application prospect of the investigated system.

In this article, the issue of exponential stability (ES) is investigated for a class of switched stochastic neural networks (SSNNs) with proportional delay (PD). The key feature of PD is an unbounded time-varying delay. By considering the comparison principle and combining the extended formula for the variation of parameters, we conquer the difficulty in consideration of PD effects for such networks for the first time, where the subsystems addressed may be stable or unstable. New delay-dependent conditions with respect to the mean-square ES of systems are established by employing the average dwell-time (ADT) technique, stochastic analysis theory, and Lyapunov approach. It is shown that the acquired minimum average dwell time (MADT) is not only relevant to the stable subsystems (SSs) and unstable subsystems (USs) but also dependent on the decay ratio (DR), increasing ratio (IR), as well as PD. Finally, the availability of the derived results under an average dwell-time-switched regulation (ADTSR) is illustrated through two numerical simulation examples.

In this article, we generalize the results on self-synchronization of Lur'e networks diffusively interconnected through dynamic relative output-feedback from the undirected graph case in Zhang et al. 2016 to the general directed graph case. A linear dynamic self-synchronization protocol of the same structure is adopted as the one proposed in Zhang et al. 2016. That is, the Lur'e-type nonlinearity is not involved in our self-synchronization protocol. It is in fact unknown and only assumed to be incrementally sector bounded within a given sector. In the absence of a leader Lur'e system defining the synchronization trajectory, we construct a novel self-synchronization manifold in order to derive the self-synchronization error dynamics. Meanwhile, the connectivity of the general directed graph having a directed spanning tree is quantified by the global connectivity, instead of the so-called general algebraic connectivity used in the directed graph case under static relative state feedback. The global connectivity plays a crucial role in handling self-synchronization problems of directed nonlinear networks via dynamic relative output feedback, including directed networks with the Lipschitz nonlinear node dynamics, which is also discussed in this article. The protocol parameter matrix design is performed by solving the obtained LMI conditions in sequence. In addition, some discussions are complemented on the important technical details in our self-synchronization protocol design along with extensions. Finally, our theoretical results are illustrated through numerical simulations over a directed nonlinear dynamical network.

This article investigates the problem of quantized fuzzy control for discrete-time switched nonlinear singularly perturbed systems, where the singularly perturbed parameter (SPP) is employed to represent the degree of separation between the fast and slow states. Taking a full account of features in such switched nonlinear systems, the persistent dwell-time switching rule, the technique of singular perturbation and the interval type-2 Takagi–Sugeno fuzzy model are introduced. Then, by means of constructing SPP-dependent multiple Lyapunov-like functions, some sufficient conditions with the ability to ensure the stability and an expected$H_{\infty }$performance of the closed-loop system are deduced. Afterward, through solving a convex optimization problem, the gains of the controller are obtained. Finally, the correctness of the proposed method and the effectiveness of the designed controller are demonstrated by an explained example.

This article investigates the stability problem for discrete-time neural networks with a time-varying delay by focusing on developing new Lyapunov-Krasovskii (L-K) functionals. A novel L-K functional is deliberately tailored from two aspects: 1) the quadratic term and 2) the single-summation term. When the variation of the discrete-time delay is further considered, the constant matrix involved in the quadratic term is extended to be a delay-dependent one. All these innovations make a contribution to a quadratic function with respect to the delay from the forward differences of L-K functionals. Consequently, tractable stability criteria are derived that are shown to be more relaxed than existing results via numerical examples.

In this article, an aperiodic sampled-data control problem is investigated for polytopic uncertain switched complex dynamical networks subject to actuator saturation. Due to the constraint on the upper bound of the sampling interval being no greater than the dwell time, the issue concerning the asynchronization between the sampled-data controller mode and the system mode is hence considered to be caused by subsystems that may switch in a sampling interval. By considering the sampling interval without switching and the sampling interval with switching, the parameters-dependent loop-based Lyapunov functionals are constructed, respectively. With the help of the constructed functional, mean-square exponential stability criteria for the error polytopic uncertain switched complex dynamical networks are presented under the definition of average dwell time. Furthermore, based on the stability criteria, the asynchronous aperiodic sampled-data controller is designed for polytopic uncertain switched complex dynamical networks subject to actuator saturation. The polytopic uncertain switched complex dynamical networks can be guaranteed to exponentially synchronize with the target node based on the proposed stability conditions and aperiodic sampled-data controller design method. Finally, by transforming the proposed theoretical conditions into the LMI-based objective optimization problem, the domain of attraction of polytopic uncertain switched complex dynamical networks is estimated. An example based on switched Chua's circuit is applied to verify the effectiveness of the proposed method.

This work is concerned with the issue of finite-time filter design for a type of Takagi-Sugeno (T-S) fuzzy Markov switching system (MSSs) with deception attacks (DAs). In view of communication network security, the randomly occurring DAs are considered in the measurement output (MO), in which the malicious unknown but bounded signals are launched by the adversary. Notably, to characterize the fallibility of the communication links between the MO and the filter, the packet dropouts, DAs, and quantization effects are taken into account simultaneously, which signifies that the resulting system is much more applicable than the existing results. Meanwhile, to deal with the phenomenon of asynchronous switching, a hierarchical structure approach is adopted, which involves the existing nonsynchronous/synchronous strategy as special cases. By means of a fuzzy-basis-dependent Lyapunov strategy, sufficient criteria are formulated such that the resulting system is stochastic finite-time boundedness under randomly occurring DAs. Finally, a double-inverted pendulum model and a numerical example are provided to validate the feasibility of the attained method.

This work investigates the observer-based asynchronous control of discrete-time nonlinear systems with network-induced communication constraints. To avoid the data collisions and side effects in a constrained communication channel, a novel dynamic event-based weighted try-once-discard (DEWTOD) protocol is proposed. In contrast to the existing protocols, the DEWTOD scheduling regulates whether the sampling instant to release and which node to transmit the sampling instant simultaneously. In light of a hidden Markov model, the time-varying detection probability matrix is characterized by a polytopic set. By resorting to the polytopic-structured Lyapunov functional, sufficient conditions are derived such that the closed-loop dynamic is mean-square exponentially stable, and the observer-based controller is designed. In the end, two numerical examples are provided to explicate the validity of the attained methodology.

In this article, an adaptive fuzzy control design strategy is presented for p -norm nontriangular stochastic high-order nonlinear systems with asymmetric output constraints and unknown nonlinearities. To prevent the violation of the asymmetric output constraint, a novel barrier Lyapunov function (BLF) is constructed. Then, combining the constructed BLF with adding a power integrator approach, the adaptive fuzzy control algorithm is developed by the backstepping technique. Simultaneously, the rigorous proof displays that the designed controller can ensure that all variables of the closed-loop system are bounded in probability with the achievement of the output constraint. Eventually, the theoretical result is further demonstrated via the simulation results.

In this study, the Takagi-Sugeno (T-S) fuzzy impulsive control problem is investigated for a class of nonlinear positive systems with time delay. The time delay under consideration is both in the continuous-time dynamics and at the impulsive instants, which can model practical systems more accurately. An impulse-time-dependent copositive Lyapunov function (IDCLF) is constructed, and the Razumikhin technique is adopted to develop conditions that ensure the globally exponential stability of T-S fuzzy positive systems with delayed impulses. The size constraint between the impulse delay and the bound of impulsive intervals is removed. A T-S fuzzy impulsive controller is designed in terms of the solutions to certain vector inequalities that are readily solvable. Numerical examples and a practical example of lipoprotein metabolism and potassium ion transfer model are given to demonstrate the effectiveness, advantages, and practicality of the proposed results.

This article is concerned with the issue of quantized sliding-mode control (SMC) design methodology for nonlinear stochastic switching systems subject to semi-Markovian switching parameters, T-S fuzzy strategy, uncertainty, signal quantization, and nonlinearity. Compared with the previous literature, the quantized control input is first considered in studying T-S fuzzy stochastic switching systems with a semi-Markovian process. A mode-independent sliding surface is adopted to avoid the potential repetitive jumping effects. Then, by means of the Lyapunov function, stochastic stability criteria are proposed to be dependent of sojourn time for the corresponding sliding-mode dynamics. Furthermore, the fuzzy-model-based SMC law is proposed to ensure the finite-time reachability of the sliding-mode dynamics. Finally, an application example of a modified series dc motor model is provided to demonstrate the effectiveness of the theoretical findings.

This article investigates a spatial-$\mathcal {L}^{\infty }$-norm-based reliable bounded control problem for a class of nonlinear partial differential equation systems in a finite-time interval. The main novelties are reflected in the following aspects: 1) inspired by the sector-nonlinearity approach, the considered nonlinear system is reconstructed by a Takagi–Sugeno fuzzy model, which provides an effective method for control design. Besides, several actuator failures, such as stuck faulty, outage faulty, and bias faulty, are taken into account and modeled by a novel Markov process; 2) partial areas’ states are sampled and transmitted based on a new distributed event-triggered communication strategy, which reduces the cost of the system design and saves the limited network resources to some extent; and 3) on the basis of the first two works, a new piecewise fuzzy controller, which requires fewer actuators compared with the distributed control method, is constructed. Then, some sufficient conditions to guarantee the finite-time boundedness (in the sense of spatial$\mathcal {L}^{\infty }$norm) and mixed$\mathcal {L}_{2}-\mathcal {L}_{\infty }/\mathcal {H}_{\infty }$disturbance attenuation performance are established, and a new linear matrix inequality relax technique is introduced to deal with the strict constraint that is caused by the asynchronous phenomenon between plant and controller. Finally, two simulation studies are given to illustrate the effectiveness and advantages of the developed controller.

In this article, the issues of finite-time synchronization and finite-time adaptive synchronization for the impulsive memristive neural networks (IMNNs) with discontinuous activation functions (DAFs) and hybrid impulsive effects are probed into and elaborated on, where the stabilizing impulses (SIs), inactive impulses (IIs), and destabilizing impulses (DIs) are taken into account, respectively. Not resembling several earlier works, a more extensive range of impulses in the context of impulsive effects has been analyzed without using the known average impulsive interval strategy (AIIS). In light of the theories of differential inclusions and set-valued map, as well as impulsive control, new sufficient criteria with respect to the estimated settling time for synchronization of the related IMNNs are established using two types of switching control approaches, which sufficiently utilize information from not only the SIs, DIs, and DAFs but also the impulse sequences. Two simulation experiments are presented to the efficiency of the proposed results.

In this research, the fuzzy adaptive event-triggered control (FAETC) issue is addressed for uncertain nonlinear networked control systems with network-induced delays (NIDs) and external disturbance. In order to effectively capture parameter uncertainties, the interval type-2 (IT-2) Takagi-Sugeno (T-S) fuzzy model is utilized to represent such a system. Considering the fact that the controller is fuzzy and the threshold can promptly update its state according to the current and latest sampled signals (SSs), it becomes quite challenging to solve the dissipative stabilization problem (DSP) with the existing schemes. Then, a novel FAETC protocol is put forward to reduce the utilization of communication resources while maintaining the desired control performance. By employing the fuzzy-logic technique and the looped Lyapunov functional (LLF) approach, sufficient conditions related to the relationship between the stabilization and desired dissipative performance for the resulting system are formulated. A numerical example is used to validate the feasibility of our attained results.

In this article, the consensus of networked underactuated robotic systems subject to fixed and switched communication networks is discussed by developing some novel event-triggered control algorithms, which can synchronously guarantee the convergence of the active states, the boundedness of the velocities of passive actuators, and the exclusion of Zeno behaviors. In the cases of fixed networks, the sufficient criteria are established for the presented distributed event-triggered mechanisms with and without using neighbors' velocities, in order to achieve a better tradeoff between the communication load and system performance. Besides, in the situation of switched networks, the sufficient criterion is established by assuming that the union of the network has a spanning tree. A distributed sampled-data rule is constructed to decide when to update its own and neighbors' estimated positions, and thus further reduces the unnecessary control cost. Finally, by further extending the main results to three other sampled-data control algorithms, several examples with performance comparisons are provided to validate the efficiency and advantages of the theoretical results.

The papers in this special section focus on recent advances in fuzzy-based intelligent Internet of Things and cyber-physical systems. The rapid development of real-time Internet of Things (IoT) applications including smart grids, smart city, and intelligent transport networks generate a tremendous amount of data from massively distributed sources, which require high computing and communication demand that frequently exceeds the users’ requirements. Furthermore, many emerging IoT applications including remote surgery, machine monitoring and control, fault detection, and healthcare generate delay-sensitive tasks, which require timely processing with minimum delay. Besides that, cyber-physical systems (CPS) integrate computing and communication capabilities with monitoring and control of entities in the physical world. These systems are usually composed of a set of networked agents, including sensors, actuators, control processing units, and communication devices. All the critical infrastructures are also a part of the cyber-physical ecosystem to enable smart and connected environments.

This article focuses on static output feedback control for fuzzy Markovian switching singularly perturbed systems (FMSSPSs) with deception attacks and asynchronous quantized measurement output. Different from the previous work, both the logarithmic quantizer and the static output feedback controller are dependent on the operation system; by means of hidden Markov models, their modes run asynchronously with that of FMSSPSs. Additionally, the deception attacks are guided by a Bernoulli variable, and nonlinear characteristics are modeled by the Takagi–Sugeno fuzzy model. By resorting to a mode-dependent Lyapunov functional, several criteria are acquired and strictly$(\mathscr {Q},\mathscr {S},\mathscr {R})\text{--}\gamma$-dissipative of FMSSPSs can be ensured. Finally, a dc motor model is expressed to illustrate the effectiveness of the asynchronous control scheme.

The output constraints are widespread in physical systems. Violation of output constraints may result in system damage and performance degradation. This article investigates the design issue of adaptive fuzzy second-order sliding-mode (SOSM) controller, which aims to handle a class of nonlinear systems with output constraints. The unknown bounds of uncertainties are approached dynamically by fuzzy logic systems. Through designing a new barrier Lyapunov function, the output constraint problem has been well solved. Then, by integrating adding a power integrator technology and adaptive fuzzy control, a novel adaptive fuzzy SOSM controller is proposed. It is proved that the proposed method makes the output variable not violate the specified constraint region. At the same time, it can be shown, based upon the Lyapunov approach, that the finite-time stability of the resulting closed-loop system under output constraint is ensured. Finally, a numerical example and a practical pendulum system are presented to demonstrate the validity of the proposed SOSM control strategy.

This article investigates the predefined-time stabilization problems of Takagi–Sugeno (T–S) fuzzy systems. For addressing the considered problems, a class of novel integral sliding mode surface is first designed based on the time-regulator function, on which the system states are forced to converge to the origin in a predefined time after the sliding mode surface is reached. Further, the proposed sliding surface is employed to construct predefined-time integral sliding mode controller for the time delayed and disturbed T–S fuzzy system. The settling time appears as the sum of two predefined-time parameters in the controller design, which, respectively, adjusts the convergence time for reaching the sliding mode surface and the convergence time for arriving the origin from the sliding mode surface. The sufficient conditions for maintaining the predefined-time stability of the T–S fuzzy systems are obtained through systematic Lyapunov stability analysis. Finally, a numerical simulation example on delayed and disturbed Chua circuit is presented to verify the effectiveness of the proposed predefined-time integral sliding mode controller.

This article is devoted to the fuzzy fault-tolerant tracking control of Markov jump systems with unknown mismatched faults. To reconstruct the faults and system states, a sequence of proportional–integral observers are established via the system outputs. With the help of a structure separation technique, the proportional–integral gains and the observer gains are solved by a unified linear matrix inequality framework. Resorting to the rebuilt faults and states from an iterative estimation algorithm, a backstepping-based fuzzy fault-tolerant tracking control scheme against the mismatched faults is established to make the resultant closed-loop system be uniformly ultimately bounded. Simulations are provided to verify the effectiveness of the proposed methods.

This article investigates the event-triggered adaptive fuzzy fixed-time secure control problem for a class of nonlinear cyber-physical systems subject to unknown deception attacks and backlashlike hysteresis. Based on an improved fractional-order command filtered backstepping method, fuzzy approximation technique, and Nussbaum gain technique, a novel adaptive practical fixed-time secure control scheme is proposed. Theoretical analyses prove that the fixed-time stability of the resulting control system and boundedness of all signals in the closed-loop system can be guaranteed by using the presented resilient controller. Especially, the given convergence time is independent of the initial states of the system and better system performance consisting of higher control accuracy and faster convergence rate can be ensured. Finally, a chemical reaction system is carried out to show the validity and superiority of the developed secure control scheme.

In this article, the fuzzy pinning adaptive event-triggered control (FPAETC) problem related to complex networked systems is investigated. In light of the fact that the underlying controllers are fuzzy pinning, and the threshold parameters can flexibly update their states based on the latest and current sampled signals rather than transmitted signals, a novel state-dependent FPAETC protocol is devised to reduce the frequency of event-triggering and save more communication resources. Moreover, a more general time-dependent AETC protocol is proposed compared to the existing related results. Then, in combination with the graph theory, looped Lyapunov functional method, and fuzzy logic pinning technique, some relaxed criteria that establish the relationship between the synchronization and desired$\mathcal {H}_{\infty }$performance for the considered system are derived. Simulation results validate the superiority of derived theoretical results.

This article investigates the problem of designing$\mathcal {H}_{\infty }$dynamic output feedback reliable controller for discrete-time Markov jump nonlinear systems with persistent dwell-time switched transition probabilities based on the Tagaki–Sugeno fuzzy model. The uncertainty of measurement output, which is assumed to occur randomly, and mode-dependent actuator faults are considered simultaneously. Moreover, the jumping property presented by system modes is described by the Markov chain of which transition probabilities are considered to be piecewise time-varying, and is described by adopting the more flexible persistent dwell-time switching rule. Based on the stochastic analysis approach and Lyapunov stability theory, some sufficient conditions are established to ensure the resulting closed-loop system being mean-square exponentially stable with the prescribed$\mathcal {H}_{\infty }$performance. Furthermore, the desired controller gains can be obtained through solving a convex optimization problem. Finally, the practicability and availability of the proposed control method are illustrated by a numerical example and a modified tunnel diode circuit model.

This article concentrates on the model-based fuzzy$l_{2}-l_{\infty }$filtering problem of a discrete-time semi-Markov jump nonlinear system. The random jumps in the studied system are governed by the discrete-time semi-Markov process. Therefore, the storage characteristics of the transition probability between systems are fully considered. To analyze the$\sigma$-mean-square stability of the filtering error system, a mode-dependent filter, which is based on the discrete-time fuzzy semi-Markov jump model, is constructed to estimate the state of the system. Thereafter, based on the Lyapunov stability theory and the discrete-time semi-Markov kernel concept, a set of sufficient criteria to ensure the$\sigma$-mean-square stability and$l_{2}-l_{\infty }$performance are derived. In addition, by using the Takagi–Sugeno fuzzy model, the nonlinear problem is effectively solved. Two illustrative examples, including a numerical example and a tunnel diode circuit example, are demonstrated to reveal the practicability of the developed filtering strategy.

This article studies the weighted memory-event-triggered$H_{\infty}$static output control issue of Takagi–Sugeno fuzzy wind turbine systems with uncertainty. To decrease the frequency of data communication, a novel adaptive memory-event-triggered mechanism is presented to choose the “necessary” control signals, which has the following two benefits. First, a weighted average signal over a historic period is utilized as the input of event-triggered scheme, instead of the current system information in the conventional one. This could reduce the control signal updating rate and avoid the false triggering events incurred by stochastic environment noises and disturbances. Second, a dynamic triggering threshold is adopted to adaptively regulate the control signal updating frequency along with the average signal. By applying the distributed delay system method to describe the weighted historic signal, a new uncertain T–S fuzzy wind turbine system with distributed delay is established. With the aid of the measured outputs and the integral inequality based on the weighting function of the average signal, the memory-event-triggered static output controller design conditions are obtained to ensure the system exponential stability and the$H_{\infty}$performance. Lastly, an experiment platform integrating Zigbee modules as the wireless network is set up to illustrate the advantages of the proposed strategy.

This article focuses on the design of fuzzy secure control for a class of coupled systems, which are modeled by a nonlinear$N$-dimensional ($N$-D) parabolic partial differential equation (PDE) subsystem and an ordinary differential equation (ODE) subsystem. Under stochastic deception attacks, a fuzzy secure control scheme is designed, which is effective to tolerate the attacks and ensure the desired performance for the considered systems. A new fuzzy-dependent Poincare–Wirtinger’s inequality (PWI) is proposed. Compared with the traditional Poincare’s inequality, the fuzzy-dependent PWI is more flexible and less conservative. Meanwhile, an augmented Lyapunov–Krasovskii functional (LKF) is newly constructed, which strengthens the correlations of the PDE subsystem and ODE subsystem. Then, on the ground of the fuzzy-dependent PWI and the augmented LKF, new exponential stabilization criteria are set up for the PDE-ODE coupled systems. Finally, a hypersonic rocket car is presented to verify the effectiveness and less conservatism of the obtained results.

This paper investigates the neuroadaptive fault-tolerant control of nonlinear vehicular platoon with unmodeled dynamics, external disturbances, time-varying actuator fault directions, and distance restrictions. For the cases of known and unknown fault directions, by combining adaptive terminal sliding mode (TSM) control technique with barrier Lyapunov function (BLF), two neuroadaptive fault-tolerant controllers are designed based on symmetric and asymmetric BLF to ensure reliability and safety of vehicular platoon. BLF approaches are adopted to avoid collisions and to maintain communication connections simultaneously. In addition, it is worth mentioning that the unfavorable symmetry assumptions in the symmetric BLF and prescribed performance methods can be removed by adopting asymmetric BLF. In the proposed scheme, we also combine the Nussbaum function to solve the influence of unknown time-varying fault directions effectively. Furthermore, the nonsingular TSM control technique and the minimum parameter approximation method in radial basis function neural network (RBFNN) are adopted to ensure that the spacing error can converge to an arbitrarily small region in finite-time. Through the Lyapunov stability theory, we prove that all signals in the closed-loop system are bounded in finite time. The proposed control schemes are validated by means of simulation examples.

In this article, we mainly consider the existence of solutions and global Mittag-Leffler stability of delayed fractional-order coupled reaction-diffusion neural networks without strong connectedness. Using the Leary-Schauder's fixed point theorem and the Lyapunov method, some criteria for the existence of solutions and global Mittag-Leffler stability are given. Finally, the correctness of the theory is verified by a numerical example.

In this article, sampled-data synchronization problem for stochastic Markovian jump neural networks (SMJNNs) with time-varying delay under aperiodic sampled-data control is considered. By constructing mode-dependent one-sided loop-based Lyapunov functional and mode-dependent two-sided loop-based Lyapunov functional and using the Itô formula, two different stochastic stability criteria are proposed for error SMJNNs with aperiodic sampled data. The slave system can be guaranteed to synchronize with the master system based on the proposed stochastic stability conditions. Furthermore, two corresponding mode-dependent aperiodic sampled-data controllers design methods are presented for error SMJNNs based on these two different stochastic stability criteria, respectively. Finally, two numerical simulation examples are provided to illustrate that the design method of aperiodic sampled-data controller given in this article can effectively stabilize unstable SMJNNs. It is also shown that the mode-dependent two-sided looped-functional method gives less conservative results than the mode-dependent one-sided looped-functional method.

This brief is concerned with the stability of a neural network with a time-varying delay using the quadratic function negative-definiteness approach reported recently. A more general reciprocally convex combination inequality is taken to introduce some quadratic terms into the time derivative of a Lyapunov-Krasovskii (L-K) functional. As a result, the time derivative of the L-K functional is estimated by a novel quadratic function on the time-varying delay. Moreover, a simple way is introduced to calculate the coefficients of a quadratic function, which avoids tedious works by hand as done in some studies. The L-K functional approach is applied to derive a hierarchical type stability criterion for the delayed neural networks, which is of less conservatism in comparison with some existing results through two well-studied numerical examples.

For the considered reaction-diffusion inertial memristive neural networks (IMNNs), this article proposes a novel gain-scheduled generalized pinning control scheme, where three pinning control strategies are involved and$2^{n}$controller gains can be scheduled for different system parameters. Moreover, a time delay is considered in the controller to make it has a memory function. With the designed controller, drive-and-response systems can be synchronized within a finite-time interval. Note that the final finite-time synchronization criterion is obtained in the forms of linear matrix inequalities (LMIs) by introducing a memristor-dependent sign function into the controller and constructing a new Lyapunov–Krasovskii functional (LKF). Furthermore, by utilizing some improved integral inequality methods, the conservatism of the main results can be greatly reduced. Finally, three numerical examples are provided to illustrate the feasibility, superiority, and practicability of this article.

This article considers neural network (NN)-based adaptive finite-time resilient control problem for a class of nonlinear time-delay systems with unknown fault data injection attacks and actuator faults. In the procedure of recursive design, a coordinate transformation and a modified fractional-order command-filtered (FOCF) backstepping technique are incorporated to handle the unknown false data injection attacks and overcome the issue of "explosion of complexity" caused by repeatedly taking derivatives for virtual control laws. The theoretical analysis proves that the developed resilient controller can guarantee the finite-time stability of the closed-loop system (CLS) and the stabilization errors converge to an adjustable neighborhood of zero. The foremost contributions of this work include: 1) by means of a modified FOCF technique, the adaptive resilient control problem of more general nonlinear time-delay systems with unknown cyberattacks and actuator faults is first considered; 2) different from most of the existing results, the commonly used assumptions on the sign of attack weight and prior knowledge of actuator faults are fully removed in this article. Finally, two simulation examples are given to demonstrate the effectiveness of the developed control scheme.

In this article, the sampled-data-based event-triggered synchronization control for fractional and impulsive complex networks (CNs) with time-varying delay is investigated and a class of more general network structure based on the switching topologies at impulsive instants is considered. First, a class of novel fractional-order integral inequalities is produced to obtain depend-delay synchronization criteria and estimate Lyapunov–Krasovskii functions. Then, a sampled-data-based event-triggered control is designed, which can ensure synchronization of fractional and impulsive CNs (FICNs) with time-varying delay. Next, by using the Lyapunov direct method, some criteria are obtained to guarantee the synchronization of FICNs. Numerical simulations are given to demonstrate that the designed sampled-data-based event-triggered synchronization strategy can effectively not only achieve synchronization of FICNs but reduce the frequency of controller update compared to the previous related works.

In this article, we investigate the parabolic partial differential equations (PDEs) systems with Neumann boundary conditions via the Takagi–Sugeno (T–S) fuzzy model. On the basis of the obtained T–S fuzzy PDE model, a novel fuzzy state controller which is associated with the boundary state of position and the mean value coefficient matrix derived through the mean value theorem of integral is designed to analyze the asymptotic stability of the parabolic PDE system. Without sampling the nonlinear parameter of the system, new stability conditions are deduced in the form of linear matrix inequalities (LMIs). Moreover, compared with the novel fuzzy state controller, more conservative conditions based on another fuzzy state controller are also provided. Finally, we explore the state-feedback controller into the Fisher equation as an application. Simulation results show that the proposed method is effective.

This article studies the distributed adaptive leader-following control for high-order time-varying nonlinear multiagent systems (MASs) with uncertain parameters. The state feedback protocol and output feedback protocol are proposed, respectively, to render all states consensus errors to converge to zero asymptotically. First, the hierarchical decomposition algorithm is used to construct a refreshed communication graph to address the mutual dependence problem of controllers. Then, by introducing a local neighborhood consensus errors-based transformation, the leader-following consensus problem is converted into the stabilization problem for the consensus error system. Using the backstepping method and tuning function technique, the distributed adaptive state feedback controller is designed to render all followers’ states to track the leader’s ones. Further, by constructing the reduced-order dynamic gain k-filter to estimate unmeasured states, a distributed adaptive output feedback controller is designed. In both controller design methods, the traditional Lipschitz condition need not be satisfied any more for all time-varying nonlinear functions, and different from most of the existing results on the high-order nonlinear MASs, full states consensus can be obtained. Finally, a general numerical example is given to illustrate the effectiveness of the proposed methods.

In this article, the distributed output-feedback adaptive fuzzy leader-following consensus is investigated for a class of high-order stochastic nonlinear multiagent systems (MASs) with unknown control gains and uncertain interactions from other agents. First, a novel dynamic gain filter is constructed to compensate unmeasured states and compared with the existing results, the number of dynamic variables is greatly reduced. Then, the tuning function method is used to construct adaptive laws to compensate unknown parameters. The uncertain interaction functions are decomposed and approximated by the fuzzy logic systems. Next, using the backstepping method, the dynamic gain filter-based distributed adaptive fuzzy leader-following protocol is designed, which only needs the dynamic variables’ information of neighbors, and the problem on computing the mutually dependent inputs in some existing results is completely avoided. Finally, the numerical simulation results are given to illustrate the effectiveness of the proposed method.

For output-constrained switched systems, uniformly local stabilization problem via state feedback is addressed. A unified tool, a common tangent-type barrier Lyapunov function (Tan-BLF), is developed to deal with the stabilization problem of switched systems and to cope with constraints imposed on system output with or without. Then, combining adding a power integrator technique (APIT) and the developed common barrier Lyapunov function, state-feedback stabilizing laws are designed systematically to make the resultant closed-loop switched systems asymptotically stable while preventing the violation of output constraints. The efficiency of the proposed method is illustrated by numerical results.

This article focuses on the design problem of fault detection (FD) observer for a class of continuous-time nonlinear systems. The$\mathcal {H_{-}}$index is employed to measure the worst cast fault sensitivity. First, the nonlinear systems are represented by the Takagi–Sugeno (T–S) fuzzy dynamic models. Then, a novel fuzzy Lyapunov function (FLF) analyzing approach is proposed to reduce the conservatism in stability analysis and$\mathcal {H_{-}}$index estimation for the T–S fuzzy model. In particular, employing this new type of the FLF, the stability analysis of the T–S fuzzy systems perfectly overcomes the difficulty of evaluating the upper bound of the time derivative of the membership function. Based on the descriptor system approach, some new sufficient conditions on the existence of fuzzy FD observers are obtained in terms of linear matrix inequalities (LMIs). Finally, illustrative examples are given to show the effectiveness of the proposed method.

In this article, the issue of positive$\mathcal {L}_{1}$filter design is investigated for positive nonlinear stochastic switching systems subject to the phase-type semi-Markov jump process. Many complicated factors, such as semi-Markov jump parameters, positivity, T–S fuzzy strategy, and external disturbance, are taken into consideration. Practical systems under positivity constraint conditions and unpredictable structural changes are characterized by positive semi-Markov jump systems (S-MJSs). First, by the key properties of the supplementary variable and the plant transformation technique, phase-type S-MJSs are transformed into Markov jump systems (MJSs), which means that, to an extent, these two kinds of stochastic switching systems are mutually represented. Second, with the help of the normalized membership function, the associated nonlinear MJSs are transformed into the local linear MJSs with specific T–S fuzzy rules. Third, by choosing the linear copositive Lyapunov function (LCLF), stochastic stability (SSY) criteria are given for the corresponding system with$\mathcal {L}_{1}$performance. Some solvability conditions for positive$\mathcal {L}_{1}$filter are constructed under a linear programming framework. Finally, an epidemiological model illustrates the effectiveness of the theoretical findings.

This article studies the adaptive finite-time quantized tracking control problem for a class of full state-constrained nonlinear systems with unknown control directions based on a modified fractional-order dynamic surface control (FODSC) technique. First, fractional calculus is introduced to filter design to avoid the issue of the “explosion of complexity” exposed in the traditional backstepping technique. To facilitate the control design, barrier Lyapunov functions and Nussbaum gain technique are utilized to handle full state-constrained problem and the unknown control directions, respectively. In addition, the fuzzy logic systems are employed to approximate the unknown nonlinearity of the system. By integrating with the approximation errors and compensating signals, a composite adaptive quantized controllers is designed to guarantee all the signals of the closed-loop systems are bounded and tracking error converges to an arbitrarily small neighborhood of the zero within a finite time. Finally, a mechanical horizontal platform model and a Brusselator model are carried out to verify the effectiveness of the presented control method.

The$\mathcal {H}_{\infty }$state estimation issue for switched delayed inertial neural networks is addressed in this work. A more universal switching law, persistent dwell-time (DT) switching law, is considered here rather than average DT one of which switching frequency among subsystems is strictly limited, or DT one. Concurrently, time delays are inevitable when transmitting information, then taking the time-varying delays into account makes the constructed systems conform well with the actual situations. The main goal in the work is devoted to designing a state estimator to ensure that the state estimation error system is globally uniformly exponentially stable and satisfies a prescribed$\mathcal {H}_{\infty }$noise attenuation level. A mixed time/mode-dependent Lyapunov–Krasovskii functional matched with the foregoing switching law is introduced. Through utilizing some reasonable inequalities and common matrix operations, some sufficient criteria which guarantee the aforesaid stability and the solvability of the addressed issue are presented. Finally, an illustrative example is provided to present the potentiality and validity of the developed results.

This article aims at designing a dynamic event-triggered$\mathcal {H}_{\infty }$load frequency controller for multi-area power systems affected by false data-injection attacks and denial-of-service attacks. A dynamic event-triggered scheme, whose threshold parameter varies with objective system states, is employed to make rational use of limited network bandwidth resources and improve the efficiency of the data utilization. Then, taking the impacts of the aforementioned hybrid cyber attacks into consideration, an attractive system model is established. Whereafter, several sufficient conditions, which can guarantee the exponential mean-square stability with a preset$\mathcal {H} _{\infty }$performance index of the studied system, are obtained through utilizing Lyapunov stability theory. Additionally, the desired controller is designed via handling convex optimization problems. Finally, a simulation example is displayed to explain the validity of the proposed method.

This article studies the robust adaptive asymptotic tracking control problem for uncertain nonlinear systems with event-triggered inputs. Contrary to the existing results, a novel nonaffine nonlinear control input separated design scheme is developed based on the Taylor decoupling technique instead of the previous approximation ways. Correspondingly, the augmented dimension parameter updated laws and the unknown bound estimation of the compounded disturbance with a positive continuous integrable function are skillfully introduced. Also, by introducing a suitable decreasing function of tracking error variables, an improved adaptive event-triggered mechanism is constructed to achieve the asymptotic tracking and the communication load can be effectively alleviated. Moreover, it is shown that all the signals in the closed-loop system are uniformly bounded and the tracking errors converge to zero in a preset compact set. Finally, the validity of the proposed method is illustrated by simulation on an inverted pendulum model.

The problem of enhanced stabilization of discrete-time Takagi–Sugeno fuzzy system is investigated in depth via proposing a new way to employ much more information implied in various time-varying differences of multi-instant normalized fuzzy weighting functions. Benefit from the given comprehensive real-time scheduling model, these time-varying differences on time vertical dimension and two different transverse dimensions are for the first time integrated into fuzzy stabilization and two key alterable weights are also introduced. Consequently, much considerable freedom can be produced and, thus, it brings much less conservative results than the reported ones in recent literature. More importantly, a very simple workable way has been provided to online recognize the specific enabled mode at each sampling instant so that those existing time-consuming online operations in referred literature can be avoided to ease online implementation costs. Indeed, this feature is very beneficial to the practical application of our theoretical results. Finally, the superiority of our developed method over previous ones is validated adequately via two benchmark simulations.

This article investigates the coordination of a class of underactuated systems subject to limited energy supply and channel bandwidth and aims to stabilize system states and exclude Zeno behaviors simultaneously. First, by means of event-triggered (E-T) and quantized techniques, several novel quantized sampled-data-based E-T schemes are constructed, which only require discrete-time controller updates and partial quantized states, and thus efficiently mitigate the control and communication workloads. Then, in order to further lower the communication consumptions, several new triggered sampled-data-based communication rules under fixed and switched networks are established, where the communications are performed only at some specific instants and, thus, the ideally continuous-time signal transmission among neighbors can be avoided. Note that sufficient criteria for achieving the coordination of the underactuated systems are derived in terms of the Lyapunov–Krasovskii functional method. Finally, numerous simulations are carried out to demonstrate the effectiveness of the theoretical results.

This study focuses on the quasisynchronization problem for reaction–diffusion neural networks (RDNNs) in the presence of deception attacks. Under deception attacks, a time–space sampled-data (TSSD) control mechanism is proposed for RDNNs. Compared with traditional control strategies, the proposed control mechanism can not only save network bandwidth but also improve the cybersecurity of communications. Inspired by Halanay’s inequality, a new inequality is proposed, which can be effectively applied to the quasisynchronization problem for dynamical systems. Then, by using this inequality and the Lyapunov functional approach, quasisynchronization criteria are set for RDNNs. The desired control gain is gained from solving a group of linear matrix inequalities. Moreover, in the absence of deception attacks, the exponential synchronization problem is studied for RDNNs. In the end, simulation results are given to demonstrate the usefulness of the theoretical analysis.

The$\mathcal {L}_{2}-\mathcal {L}_{\infty }$filter design for linear stochastic time-varying delay systems based on the filter velocity constraint is considered in this dissertation. Different from existing$\mathcal {L}_{2}\,-\,\mathcal {L}_{\infty }$performance analysis and filter design, a new criterion is proposed to make a more accurate judgment, which can not only guarantee the stability of the filtering error system with$\mathcal {L}_{2}-\mathcal {L}_{\infty }$performance level but also can estimate the convergence speed of the filter. The ideal filter should has the same convergence speed as the original system, so as to ensure the rapid convergence of the error system, but also not to reduce the anti-interference ability and increase the bandwidth of the filter system. By the new filter design method, an ideal filter can be designed, which can regulate the convergence speed of the filter system to the desired effect. A simulation instance is provided to exhibit the validity of the new approach.

In this study, the problem of fault-tolerant sampled-data H∞ control for a networked control system with random time delays and actuator faults is investigated. Stochastic variables conforming with the Bernoulli distribution are considered to depict random time delays. A state feedback sampled-data controller is designed to ensure the asymptotical stability and H∞ performance of the resulting closed-loop system. By applying the Lyapunov–Krasovskii stability theory and the reciprocally convex combination lemma, a stability criterion for a random time-varying delay system is developed that guarantees the designed controller can satisfy the requirements of stability and maneuverability. The desired controller gain is then found based on the linear matrix inequalities. Finally, as a real application, a quarter-vehicle suspension system model is provided to demonstrate the benefits and validity of the proposed control law.

This paper investigates the state estimation for switched memristive neural networks with nonhomogeneous sojourn probabilities. Essentially different from most current literature, a novel switching law is developed to depict the dynamic behavior of switched memristive neural networks, in which the sojourn probabilities of each subsystem are assumed to be nonhomogeneous, and a higher-level deterministic switching signal is proposed to regulate proper feedback switching information by means of the average dwell time approach. Meanwhile, to alleviate the constraint network bandwidth resource efficiently, a dynamic event-triggered mechanism with a novel threshold parameter is proposed in determining if the current data should be released or not. By resorting to the Lyapunov functional technique and the stochastic analysis strategy, some sufficient conditions are addressed to ensure the stochastic stability of the augmented switched memristive neural networks. In the end, the effectiveness and superiority of the developed results are verified by a numerical example.

This paper considers the control problem of p-norm stochastic nonlinear systems with output constraints, while the system nonlinearities are completely unknown and the system states are unavailable except the output. A nonlinear observer is constructed to estimate the unmeasurable states. Then, based on the constructed observer and a tan-type barrier Lyapunov function (BLF), an adaptive fuzzy output-feedback control strategy is developed by combining the technique of adding a power integrator with the fuzzy logic systems (FLSs). The proposed scheme enables that all the signals of the considered closed-loop systems are bounded in probability while the prespecified output constraint is not violated. Finally, a numerical example verifies the validation of the proposed scheme.

In this paper, the H∞stabilization problem is studied for discrete-time semi-Markov jump singularly perturbed systems (SMJSPSs) with repeated scalar nonlinearities. As the exact statistical information of the sojourn time or the mode transition is difficult to obtain, the case with only partial semi-Markov kernel information available is considered. Furthermore, introducing an external disturbance or nonlinearity into the analysis of discrete-time semi-Markov jump systems (DTSMJSs) meets critical obstacles, since the relation between the system state vectors at two nonadjacent instants is difficult to determine. To address this issue, the variation trend of the Lyapunov function for a semi-Markov jump sequence is analyzed in detail. Subsequently, criteria of mean-square exponential stability (MSES) for DTSMJSs are established for the first time based on the Lyapunov stability theory. By virtue of the criteria obtained and the cone complementary linearization algorithm, a controller ensuring MSES and H∞performance for discrete-time nonlinear SMJSPSs is constructed. Finally, the effectiveness and applicability of the proposed method are validated by simulation examples including an inverted pendulum model.

The dissipative stability problem for a class of Takagi-Sugeno (T-S) fuzzy systems with variable sampling control is the focus of this paper. The controller signals are assumed to transmit with a constant delay. Our aim is to design the sampled-data controller such that the T-S fuzzy system is globally asymptotically stable with a (Q,S,R) - γ -dissipative performance index. The stability is analyzed by using a novel piecewise Lyapunov-Krasovskii functional (LKF) together with a looped-functional and free-matrix-based (FMB) inequality method. First, several useful linear matrix inequality (LMI) conditions are derived to verify the dissipative stability of the T-S fuzzy system and then the controller gains matrices are expressed by resorting the LMI approach with the maximal-allowable upper bound (MAUB) of sampling periods. The proposed LMI conditions can be easily solved by using the MATLAB tool box. Finally, the numerical example of a truck-trailer system is considered and analyzed by the proposed scheme to illustrate the benefit and superiority.

This article studies a cooperative output-feedback secure control problem for distributed cyber-physical systems over an unreliable communication interaction, which is to achieve coordination tracking in the presence of intermittent denial-of-service (DoS) attacks. Under the switching communication network environment, first, a distributed secure control method for each subsystem is proposed via neighborhood information, which includes the local state estimator and cooperative resilient controller. Second, based on the topology-dependent Lyapunov function approach, the design conditions of secure control protocol are derived such that cooperative tracking errors are uniformly ultimately bounded. Interestingly, by exploiting the topology-allocation-dependent average dwell-time (TADADT) technique, the stability analysis of closed-loop error dynamics is presented, and the proposed coordination design conditions can relax time constraints on interaction topology switching. Finally, two numerical examples are presented to demonstrate the effectiveness of the theoretical results.

In this article, an adaptive fuzzy tracking control scheme is developed for a class of uncertain switched nonlinear systems with input saturations and full-state constraints. First to surmount the design difficulty with respect to a saturation nonlinearity controller, a nonlinear smooth function approximating the nondifferential saturation function is introduced to establish a standard switched adaptive tracking control strategy based on the mean-value theorem and the input compensation technique. Then, invoking fuzzy-logic systems (FLSs), a novel analysis method of average dwell time for switched nonlinear systems with full-state constraints is proposed by using an artful logarithmic inequality. Furthermore, the designed adaptive controller can ensure that all the states of uncertain switched nonlinear systems are not to violate the set constraint bounds by employing barrier Lyapunov functions (BLFs), and that the system output tracking error can converge to a desired neighborhood of the origin within a suitable compact set. Finally, the simulation results are given to demonstrate the validity of the presented approach.

This article focuses on the exponential synchronization problem of T-S fuzzy reaction-diffusion neural networks (RDNNs) with additive time-varying delays (ATVDs). Two control strategies, namely, fuzzy time sampled-data control and fuzzy time-space sampled-data control are newly proposed. Compared with some existing control schemes, the two fuzzy sampled-data control schemes cannot only tolerate some uncertainties but also save the limited communication resources for the considered systems. A new fuzzy-dependent adjustable matrix inequality technique is proposed. According to different fuzzy plant and controller rules, different adjustable matrices are introduced. In comparison with some traditional estimation techniques with a determined constant matrix, the fuzzy-dependent adjustable matrix approach is more flexible. Then, by constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the fuzzy-dependent adjustable matrix approach, new exponential synchronization criteria are derived for T-S fuzzy RDNNs with ATVDs. Meanwhile, the desired fuzzy time and time-space sampled-data control gains are obtained by solving a set of linear matrix inequalities (LMIs). In the end, some simulations are presented to verify the effectiveness and superiority of the obtained theoretical results.

This article concentrates on the nonstationary control for a class of nonlinear Markovian switching systems (MSSs) with the Tagaki-Sugeno (T-S) fuzzy model. By adopting variable quantization density, a more general type of T-S fuzzy MSSs is established, in which the varies performance demands can be satisfied in a more flexible way. The major benefit of the developed method lies that a multiple hierarchical structure strategy is developed concerning nonsynchronization among the original system, controller, and quantizer, which covers the reported asynchronous/synchronous mechanisms as special cases. On the basis of fuzzy-basis-dependent and mode-dependent Lyapunov function, some sufficient conditions are derived and controller gains are designed. Finally, the feasibility of the achieved result is verified by a practical example.

This article investigates the adaptive fuzzy control design for p-norm stochastic high-order lower triangular nonlinear systems with output constraints and unknown nonlinearities. First of all, a tan-type barrier Lyapunov function (BLF) is constructed to deal with the output constraint issue. Subsequently, an adaptive fuzzy control algorithm is developed by combining the constructed BLF with adding a power integrator technique. Simultaneously, the Lyapunov analysis shows that the designed controller can guarantee the boundness of all the variables in the closed-loop system in probability without violating the given output constraint. Finally, some comparative simulation results are provided to demonstrate the effectiveness of the proposed method.

This article investigates the memory adaptive event-triggered fault detection and isolation (FDI) problem for nonlinear networked control systems under periodic denial-of-service (DoS) attacks, where the nonlinear systems are described by Takagi–Sugeno (T–S) fuzzy models with unknown membership functions. First, a novel event-triggered mechanism is proposed to save communication resources. The triggering threshold is adaptively adjusted by multiple previous sampled data, not only depending on the latest triggering data. Second, taking DoS attacks, and event-triggered mechanism into consideration, a switching state-feedback controller is established, and the exponential stability is derived. Meanwhile, the controller, and the event-triggered mechanism are simultaneously developed based on a piecewise Lyapunov function. Then, a set of switching T–S fuzzy observers are constructed to realize FDI under DoS attacks. Besides, a switching variable method is introduced to address the asynchronous premise variables problem caused by the event-triggered mechanism. Finally, simulation cases are given to demonstrate the validity, and merit of the proposed FDI scheme.

This article investigates the nonfragile extended dissipative control problem for a class of nonlinear fast sampling singularly perturbed systems subject to Markov jumping parameters. Thereinto, the Tagaki-Sugeno fuzzy model is utilized to describe the nonlinear part, and the nonfragile control strategy is employed to handle the gain variations in a controller. Furthermore, we aim to design a fuzzy nonfragile controller, which can ensure that the systems under consideration are stochastically stable as well as extended dissipative. To this end, through constructing a mode-dependent Lyapunov function and applying the convex optimization theory, some sufficient conditions that are independent of the singular perturbation parameter ∈ are derived. Besides, a set of controller gains is also obtained by solving the aforesaid conditions. Based on the effectiveness of the controller, the technique to obtain the upper bound of the singular perturbation parameter is developed, and an improved system performance related to such an upper bound is achieved. Finally, through a numerical example and an inverted pendulum model example, the superiority and the practicability of this article are verified.

This article investigates the adaptive output feedback resilient control problem for a class of incommensurate fractional-order (FO) nonlinear systems in the presence of time-varying delays and actuator faults by combining with an adaptive backstepping technique and a modified FO dynamic surface control (FODSC) method. Considering that the information of system states is not fully available, a hybrid fuzzy observer is designed to estimate the unmeasurable system states, where the neuro-fuzzy network system is introduced to handle the unknown nonlinear functions existing in the system. Furthermore, in order to overcome the problem of the explosion of complexity caused by traditional backstepping design procedure, an FO filter is constructed to pass the virtual control signal based on the FODSC scheme. Moreover, according to an indirect Lyapunov stability method, an adaptive hybrid fuzzy output feedback controller is designed to guarantee that all the signals of the closed-loop systems are bounded. Finally, three examples are given to verify the validity and superiority of the presented control scheme.

In this article, an investigation about the issue of fuzzy event-triggered asynchronous dissipative control for T–S fuzzy Markov jump systems (FMJSs) with unknown transition probabilities and nonuniform sampling is conducted. First of all, a mode-dependent looped Lyapunov–Krasovskii functional (LKF) is proposed, which takes full use of the available information not only from sawtooth structure characteristics but also from the inner sampling dynamics. Meanwhile, a hidden Markov chain is employed to depict the mismatch between the original system and the state-dependent fuzzy event-triggered controller. Then, based on the LKF methodology, matrix inequality techniques, and the reciprocally convex combination protocol, some relaxed criteria with respect to the stochastic stable of the considered system and the desired dissipative performance are derived, simultaneously. A numerical experiment is given to illustrate the significance of the theoretical results.

This article investigates the nonfragile$\mathcal {H}_{\infty }$synchronization issue for a class of discrete-time Takagi–Sugeno (T–S) fuzzy Markov jump systems. With regard to the T–S fuzzy model, a novel processing method based on the matrix transformation is introduced to deal with the double summation inequality containing fuzzy weighting functions, which may be beneficial to obtain conditions with less conservatism. In view of the fact that the uncertainties may occur randomly in the execution of the actuator, a nonfragile controller design scheme is presented by virtue of the Bernoulli distributed white sequence. The main novelty of this article lies in that the transition probabilities of the Markov chain are considered to be piecewise time-varying, and whose variation characteristics are described by the persistent dwell-time switching regularity. Then, based on the Lyapunov stability theory, it is concluded that the resulting synchronization error system is mean-square exponentially stable with a prescribed$\mathcal {H}_{\infty }$performance in the presence of actuator gain variations. Finally, an illustrative example about Lorenz chaotic systems is provided to show the effectiveness of the established results.

This article is concerned with the fuzzy adaptive tracking control problem for a class of uncertain nonaffine nonlinear systems with full state constraints and event-triggered inputs. First, based on the mean-value theorem and the input compensation method, the control input signal of nonaffine nonlinear term is successfully decoupled, and the remained compound nonlinear function can be approximated by fuzzy logic systems. Then, an improved event-triggered control strategy including the fixed threshold, the event-trigger control input, and the decreasing function of tracking errors, is given to update the actual control input. Correspondingly, the computation burden of the communication procedure is effectively alleviated. In addition, the developed control scheme guarantees that all the states of the closed-loop systems are not to violate the predefined compact sets by constructing barrier Lyapunov functions. Also, the system output tracking error can converge to a desired neighborhood of the origin. Finally, the validity of the proposed approach is demonstrated by the simulation results.

In this article, the dissipativity-based sampled-data control problem is studied for the fuzzy switched Markovian jump systems (FSMJSs). By considering the problem of the asynchronous switching caused by the subsystems' switching occurred during the sampling intervals, constructing novel time-dependent Lyapunov functional, and using average dwell time (ADT) approach, sufficient mean-square exponential stability criteria, and strictly (Q,S,R)-γ- dissipativity criteria are proposed under the constraints that the maximum sampling period is no larger than the dwell-time between the transition rates switching instants. Meanwhile, a relationship between ADT and sampling period is revealed for FSMJSs. Based on the strictly (Q,S,R)-γ- dissipativity criteria, the sampled-data controller is designed for FSMJSs. A circuit simulation example is provided to illustrate the effectiveness of the proposed method.

This article proposes an enhanced switching stabilization of Takagi-Sugeno fuzzy systems by developing a new fuzzy switching controller. A distinctive feature of the proposed approach is the enhanced ability to utilize the updated information of the normalized fuzzy weighting functions at each sampling point. Compared with those recent results in the literature, two key performance indexes, i.e., reducing the conservatism and alleviating the online computational burden, can be improved together while the off-line computational burden increases as a tradeoff but is still affordable from the point of algorithmic complexity. As a result, the efficiency of fuzzy stabilization is enhanced and thus its scope of application can be enlarged much more than before. Finally, the superiority of the proposed approach is illustrated by some simulation comparisons.

This article focuses on the design of a fuzzy adaptive event-triggered sampled-data control (AETSDC) scheme for stabilization of Takagi-Sugeno (T-S) fuzzy memristive neural networks (MNNs) with reaction-diffusion terms (RDTs). Different from the existing T-S fuzzy MNNs, the reaction and diffusion phenomena are considered, which make the presented model more applicable. A fuzzy AETSDC scheme is proposed for the first time, in which different AETSDC mechanisms will be applied for different fuzzy rules. For each fuzzy rule, the corresponding AETSDC mechanism can be promptly adaptively adjusted based on the current and last sampled signals. So the fuzzy AETSDC scheme can effectively save the limited communication resources for the considered system. By introducing a suitable Lyapunov- Krasovskii functional, new stability and stabilization criteria are established for T-S fuzzy MNNs with RDTs. Meanwhile, the desired fuzzy AETSDC gains are obtained. Finally, simulation results are given to verify the superiority of the fuzzy AETSDC scheme and the effectiveness of the theoretical results.

This article focuses on the adaptive synchronization for a class of fractional-order coupled neural networks (FCNNs) with output coupling. The model is new for output coupling item in the FCNNs that treat FCNNs with state coupling as its particular case. Novel adaptive output controllers with logarithm quantization are designed to cope with the stability of the fractional-order error systems for the first attempt, which is also an effective way to synchronize fractional-order complex networks. Based on fractional-order Lyapunov functionals and linear matrix inequalities (LMIs) method, sufficient conditions rather than algebraic conditions are built to realize the synchronization of FCNNs with output coupling. A numerical simulation is put forward to substantiate the applicability of our results.

This brief is devoted to exploring the global Mittag-Leffler (ML) synchronization problem of fractional-order memristor neural networks (FOMNNs) with leakage delay via a hybrid adaptive controller. By applying Fillipov's theory and the Lyapunov functional method, the novel algebraic sufficient condition for the global ML synchronization of FOMNNs is derived. Finally, a simulation example is presented to show the practicability of our findings.

Finite-time synchronization (FTS) is discussed for delayed semi-Markov switching neural networks (S-MSNNs) with quantized measurement, in which a logarithmic quantizer is employed. The stochastic phenomena of structural and parametrical changes are modeled by a semi-Markov process whose transition rates are time-varying to depend on the sojourn time. Practical systems subject to unpredictable structural changes, such as quadruple-tank process systems, are described by delayed S-MSNNs. A key issue under the consideration is how to design a feedback controller to guarantee the FTS between the master system and the slave system. For this purpose, by using the weak infinitesimal operator, sufficient conditions are constructed to realize FTS of the resulting error system over a finite-time interval. Then, the solvability conditions for the desired finite-time controller can be determined under a linear matrix inequality framework. Finally, the theoretical findings are illustrated by the quadruple-tank process model.

This article is concerned with a neural adaptive tracking control scheme for a class of multiinput and multioutput (MIMO) nonaffine nonlinear systems with event-triggered mechanisms, which include the fixed thresholds, triggering control inputs, and decreasing functions of tracking errors. Unlike the existing results of nonaffine nonlinear controller decoupling, a novel nonlinear multiple control inputs separated design method is proposed based on the mean-value theorem and the Taylor expansion technique. By this way, a weaker condition of nonlinear decoupling is provided to instead of the previous ones. Then, introducing a prescribed performance barrier Lyapunov function (PPBLF) and using neural networks (NNs), the presented event-triggered controller can maintain better tracking performance and effectively alleviate the computation burden of the communication procedure. Furthermore, it is proved that all the closed-loop signals are bounded and the system output tracking errors are confined within the prescribed bounds. Finally, the simulation results are given to demonstrate the validity of the developed control scheme.

This article focuses on the design of an adaptive event-triggered sampled-data control (ETSDC) mechanism for synchronization of reaction-diffusion neural networks (RDNNs) with random time-varying delays. Different from the existing ETSDC schemes with predetermined constant thresholds, an adaptive ETSDC mechanism is proposed for RDNNs. The adaptive ETSDC mechanism can be promptly adaptively adjusted since the threshold function is based on the current sampled and latest transmitted signals. Thus, the adaptive ETSDC mechanism can effectively save communication resources for RDNNs. By taking the influence of uncertain factors, the random time-varying delays are considered, which belongs to two intervals in a probabilistic way. Then, by constructing an appropriate Lyapunov-Krasovskii functional (LKF), new synchronization criteria are derived for RDNNs. By solving a set of linear matrix inequalities (LMIs), the desired adaptive ETSDC gain is obtained. Finally, the merits of the adaptive ETSDC mechanism and the effectiveness of the proposed results are verified by one numerical example.

This article addresses the problem of finite-time stabilization for a class of high-order nonlinear stochastic systems with asymmetric output constraints. A novel barrier Lyapunov function (BLF) is first presented to handle such asymmetric constraints. Further, based on the proposed BLF and the adding a power integrator technique, a controller design approach is developed by the backstepping method. It can be rigorously proved that the designed controller can not only make the system states finite-time converge to the origin in probability but also ensure that the constraint on system output is not violated. Another novelty of this approach is that it is a unified tool owing to its simultaneous application to the systems without output constraints. Finally, the validity of the proposed scheme can be verified by a simulation example.

In this paper, we address the consensus tracking problem for the multiagent system (MAS) based on a nonfragile memory sampled-data controller. Considering the effect of controller gain fluctuation and communication delay, a novel sampled-data control scheme with variable sampling interval is designed for each agent. By developing some new terms, an improved piecewise Lyapunov-Krasovskii functional (LKF) is constructed to take full advantage of characteristic about real sampling pattern. Furthermore, some relaxed matrices constructed in the LKF are not necessarily positive definite. Making full use of the LKF and free-matrix-based integral inequality, some sufficient criteria are developed to ensure the consistency of the MAS. Then, by solving a group of linear matrix inequalities with the maximal sampling interval, the desired sampled-data control gain matrix is obtained. Finally, the numerical example of a 5-agent system is given to illustrate the effectiveness of the proposed approach in this paper.

This article studies the security of networked interconnected systems (NISs) subject to cyber-attacks based on a new event-triggered mechanism (ETM). NISs with spatially distributed subsystems are vulnerable to cyber-attacks. With a new concept of security control, attention is focused on designing a novel ETM together with a decentralized output feedback control (DOFC) scheme such that the NIS subject to cyber-attacks is stable in secure sense. Under the proposed ETM, the average data-releasing rate over the whole operating period can be extremely decreased, thereby reducing the burden of network bandwidth, computation, and battery-supply. Moreover, during the system with external disturbance or attack on the communication network, more transmission-events can be generated than other periods. As a result, the desired control performance can be achieved. By using stochastic analysis techniques and Lyapunov stability theory, sufficient conditions are derived to obtain both the controller gains and the parameters of the ETM. Numerical simulation of chemical reactor systems is given to illustrate the advantages and effectiveness of the proposed theories and design techniques.

In this article, theH∞static output feedback tracking control problem is studied for discrete-time nonlinear networked systems subject to quantization effects and asynchronous event-triggered constraints. The Takagi-Sugeno (T-S) fuzzy model is utilized to represent the investigated nonlinear networked systems. A novel asynchronous event-triggered strategy is given to reduce the network communication burdens in both communication channels from the plant to the controller and from the reference model to the controller. The objective of this article is to design a quantized event-triggered tracking controller such that the resulting system is asymptotically stable and the givenH∞tracking performance is guaranteed. The sufficient design conditions for the tracking controller are formulated in the form of the linear matrix inequalities (LMIs). Furthermore, a simulation example will be utilized to show the effectiveness of the developed design strategy.

This paper investigates the controller design problem of networked control systems subject to cyber attacks. A hybrid-triggering communication strategy is employed to save the limited communication resources. State measurements are transmitted over a communication network and may be corrupted by cyber attacks. The aim of this paper is to design a controller for a new closed-loop system model with consideration of randomly occurring cyber attacks and the hybrid-triggering scheme. A stability criterion is obtained for the system stabilization by employing Lyapunov stability theory and stochastic analysis techniques. Moreover, the desired controller gain is derived by resorting to some matrix inequalities. Finally, a numerical example is exploited to demonstrate the usefulness of the proposed scheme.

This paper is concerned with the asynchronous fault detection and isolation (FDI) strategy in finite frequency domain for Markov jump systems (MJSs) with actuator failures under networked environment. A set of binary-valued Bernoulli distributed sequences is introduced to describe the transmission and the scheduling of the sensor nodes. Then, an MJS directing by two Markov process and with multiple stochastic parameters is derived to characterize the whole system dynamics. Subsequently, by means of the stochasticH-index in finite frequency domain and the geometric mapping approach, an asynchronous FDI scheme is investigated, in which the filters complete the FDI mission with only partial information of the measurements. Meanwhile, each FDI filter is only sensitive to one possible actuator fault and decoupled from others. Then the existence of the desired filters is ensured by some novel sufficient conditions. Finally, an application example is presented to show the effectiveness of the proposed theoretical results.

In this article, the global adaptive fault-tolerant control is considered for a class of uncertain nonlinear systems with sensors, actuator faults, and disturbance. Different from the previous results, there are no a priori bounds of the sensor sensitivity, actuation effectiveness, and disturbance. A switching-type adaptive controller is designed where the controller parameter is tuned online by the proposed switching mechanism. It has also been proven that global stability can be achieved under the designed controller. Finally, a simulation example is provided to illustrate the effectiveness of the proposed method.

This paper concerns a positive G1 observer for positive nonlinear semi-Markovian switching systems (MSSs) via the expansion of Taylor formula and the fuzzy Lyapunov function approach, in which semi-Markovian switching parameters, positivity, Takagi-Sugeno (T-S) fuzzy, and external disturbance are all considered in a unified framework. A fuzzy Lyapunov function approach with less conservativeness is introduced into the research of positive systems. In the system under consideration, positive S-MSSs with the semi-Markovian process can describe more complex systems in a practical control process. The main motivation of this paper is that the practical system subject to positivity and abrupt changes can be described by positive nonlinear S-MSSs, which always needs to consider the external disturbance. First, by using the normalized membership function approach, positive nonlinear S-MSSs can be represented by local positive T-S fuzzy S-MSSs. Second, by constructing the fuzzy Lyapunov function, some sufficient conditions are proposed for stochastic stability and G1-gain performance analysis, respectively. Then, a positive G1 observer in a novel standard linearprogramming condition is designed to guarantee the resulting closed-loop augmented system is positive and stochastically stable with a required G1-gain performance. Finally, a practical example about the epidemiological model is introduced to show the effectiveness of the main theory.

This article concentrates on the global uniform exponential stability analysis and dissipativity property for a class of discrete-time gene regulatory networks (GRNs). To describe the processes at work as cells change phenotype, the time-dependent persistent dwell-time switching strategy is applied to switched GRNs. Formulating the mode-dependent Lyapunov-Krasovskii functional, the global uniform exponential stability and strictly dissipativity property criteria for the GRNs subject to time-varying delays are presented. Ultimately, an example, including three cases is employed to illustrate and discuss the availability of the given criteria.

This article studies the adaptive backstepping control problem for a class of fractional-order (FO) nonlinear systems subject to input quantization and unknown control directions by combining with an indirect FO Lyapunov stability method, and a command filter-based FO dynamic surface control (FODSC) technique. First, a modified FODSC method is utilized to reduce the computational complexity existing in the conventional recursive procedure in which an FO command filter is designed to obtain the command signals and their FO derivatives. Furthermore, the Nussbaum function and neuro-fuzzy networks (NFNs) are adopted to deal with the problem of the unknown control directions and unknown nonlinear functions existing in the system. Moreover, by introducing the compensation and prediction mechanism to controller design, the adaptive controllers, and adaptive laws are constructed to ensure that all the signals of the controlled systems are bounded. Finally, two examples are given to show the validity of the developed control method.

This article investigates adaptive control and exponential synchronization of a kind of derivative coupled complex dynamical networks (CDNs) with proportional delay. Based on impulsive control and adaptive pinning control protocols, sufficient criteria for achieving of the exponential synchronization on CDNs are obtained by jointly applying the proportional delayed impulsive comparison principle, the extended parameters variation formula and the definition of average impulsive interval. Meanwhile, suitable control gains for achieving adaptive synchronization are acquired according to the efficiently designed adaptive updating laws. In addition, the convergence velocity of the exponential synchronization is precisely estimated. Finally, one numerical simulation is presented to illustrate the validity of the adaptive pinning control protocols and theoretical results. By introducing the concept of impulsive distance for the first time, the example further explains the dynamic balance between the impulsive effects and the feedback control gains, which provides a method in controller designing.

This article is devoted to study the cluster synchronization for a kind of complex dynamical networks consisting of nonidentical nonlinear Lur'e systems. Different from general time delays, a proportional delay is taken into consideration in this article, which is a kind of unbounded time-varying delays and thus largely increases the difficulty in network synchronization. In consideration of the topology structures of the Lur'e networks, an effective impulsive pinning controller is proposed, which will be placed on the Lur'e systems having directed paths with those in the other clusters. Considering different functional roles that the impulsive effects play, sufficient criteria for the cluster synchronization of the nonidentically coupled Lur'e dynamical networks are obtained by applying the proportionally delayed impulsive comparison principle, the concept of average impulsive interval, and the extended parameters variation formula. Simultaneously, the exponential convergence rates are successfully estimated with respect to different functions of the impulsive effects. In the end of this article, three numerical simulations are proposed to denote the effectiveness of the control protocols and the theoretical results.

In this paper, the issue on cluster synchronization (CS) of complex networks (CNs) with parameter mismatches and time-varying delays (TVDs) is investigated. A memory pinning impulsive control (MPIC) approach, which is dependent not only on the current state and the history state but also on the number of nodes to be controlled at impulsive instants, is proposed for the first time. By resorting to the effective Lyapunov functional and impulsive differential inequalities, some criteria for achieving the CS of the concerned networks are established. Compared with the existing results, the network nodes are nonidentical and two MPIC cases are taken into account. Two numerical examples based on MATLAB software are presented to demonstrate the effectiveness of the proposed MPIC approach.

This article considers integral sliding-mode control (ISMC) for multiagent systems with matched disturbances through adaptive dynamic programming (ADP) method. A distributed disturbance observer (DDO) is designed for each agent to generate the disturbance estimation. Using the disturbance estimation, an ISMC scheme is proposed to reject the input disturbance and obtain the equivalent local neighbor consensus sliding-mode dynamics. Then, a discount performance function is designed for each agent, and ADP technique is utilized to address the optimal consensus control for the equivalent sliding-mode dynamics online. Based on the gradient descent algorithm, we propose the adaptive weight tuning laws for the critic-actor neural networks (NNs) to carry out the ADP method. Furthermore, the local neighbor consensus errors and the weight estimation errors for the critic-actor NNs are proved to be uniformly ultimately bounded (UUB). Finally, the practical example is applied to validate the effectiveness of our results.

This paper focuses on the state estimator design problem for a switched neural network (SNN) with probabilistic quantized outputs, where the switching process is governed by a sojourn probability. It is assumed that both packet dropouts and signal quantization exist in communication channels. Asynchronous estimator and quantification function are described by two different hidden Markov model between the SNNs and its estimator. To deal with the small uncertain of estimators in a random way, a probabilistic nonfragile state estimator is introduced, where uncertain information is described by the interval type of gain variation. A sufficient condition on mean square stable of the estimation error system is obtained and then the desired estimator is designed. Finally, a simulation result is provided to verify the effectiveness of the proposed design method.

This article addresses the issue of asynchronous partially mode-dependent filtering for networked Markov switching repeated scalar nonlinear systems (MSRSNSs) subject to quantized measurements (QMs). Especially, a novel partially mode-dependent filter (PMDF) is constructed, where the signal transmission of a filter mode occurred randomly and is modeled by a Bernoulli distributed sequence. The designed PMDF is different from state mode, which is governed by an asynchronous switching rule. By utilizing a diagonally dominant-type Lyapunov functional (DDTLF), sufficient conditions ensure that the existence of the PMDF and the ℓ2-l∞performance index are derived. Finally, an economic example is adopted to substantiate the applicability of the developed theoretical results.

filtering issue for a class of continuous-time switched neural networks (NNs). Our aim is to design a mode-dependent filter acquiring the state of the investigated system, and ensuring the global uniform exponential stability of the resulting filtering error system. The persistent dwell-time (PDT) switching strategy is employed to represent the switching among NNs. By utilizing a suitable Lyapunov function and the switched system theory, some criteria for the solvability of the addressed problem are presented under the full consideration of switching frequency. Finally, the filter gains are derived by a straightforward decoupling method, and with the aid of the algorithm of the continuous-time PDT switching regularity, the availability of the filter is expounded through a numerical example.

This paper deals with the issue of sampled-data stabilization for T-S fuzzy systems (TSFSs) with state delays and nonuniform sampling. First, a fuzzy membership function (FMFs)-dependent approach is proposed, which uses information not only on both the delayed state and actual sampling pattern but also on the FMFs. Second, the inner sampling interval is split into flexible terminals, and a novel FMFs-dependent Lyapunov-Krasovskii functional (LKF) is constructed. Meanwhile, a fuzzy sampled-data controller (FSDC) with a switched topology is designed to solve the tricky issue on the estimation of FMFs-dependent terms. Then, based on the LKF methodology, the extended Wirtinger's inequality, and an improved reciprocally convex combination strategy, some relaxed criteria with both a larger sampling period and upper bound of time delays for achieving the stabilization of TSFSs are derived. Two numerical examples are presented to demonstrate the superiority and applicability of the proposed scheme.

In this article, the adaptive fault-tolerant tracking control problem of nonaffine stochastic nonlinear systems with actuator failures and full-state constraints is studied. To surmount the design difficulty from nonaffine nonlinear term with multi-input and single-output (MISO) faulty modes, a novel nonlinear fault compensation function with adjustable parameter factor is first introduced to establish a standard adaptive fault-tolerant control (AFTC) strategy based on the mean-value theorem. Then, the remaining nonlinear function, including the partial loss of effectiveness, outage, and stuck cases, together with the constructed compound nonlinear function can be approximated by using the suitable fuzzy-logic system (FLS). Moreover, it is shown that all the states of nonaffine stochastic nonlinear systems are not violating the preset constraint bounds by employing the barrier Lyapunov functions (BLFs). Also, the given adaptive controller can guarantee all the closed-loop signals are uniformly ultimately bounded (UUB) in probability in the sense of fourth-moment within the appropriate compact sets. Finally, two simulation examples are given to demonstrate the validity of the proposed method.

The problem of observer-based state estimation of discrete-time fuzzy systems is investigated by constructing a joint switching mechanism for adjacent instants. Thanks to the usage of both the proposed spatial partitioning method and a set of new free matrix-valued variables, abundant information about size differences of all the normalized fuzzy weighting functions for adjacent instants can be interactively integrated into the fuzzy observer design for the first time. Compared with the recent result from three important aspects (conservatism level, online computational burden, and offline computational burden), three positive results can be obtained: 1) it provides a chance for reducing the conservatism by a large margin; 2) the required online computational burden remains unchanged as referred ones; and 3) as a tradeoff, the required offline computational burden increases to some extent but is still affordable from the view of complexity analysis. Finally, two numerical simulations have been given to validate the effectiveness of our developed theoretical results.

This paper investigates finite-horizon optimal consensus control problem for unknown multiagent systems with state delays. It is well known that optimal consensus control is the solutions to the coupled Hamilton-Jacobi-Bellman (HJB) equations. An off-policy reinforcement learning (RL) algorithm is developed to learn the two-stage optimal consensus solutions to the coupled time-varying HJB equations using the measurable state data instead of the knowledge of the state-delayed system dynamics. Subsequently, for each agent, a single critic neural network (NN) is utilized to approximate the time-varying cost function and help to calculate optimal consensus control policy. Based on the method of weighted residuals, adaptive weight update laws for the critic NNs are proposed. Finally, the simulation results are provided to illustrate the effectiveness of the proposed off-policy RL method.

This paper is concerned with the reliable extended passive control problem for semi-Markov jump Takagi-Sugeno (T-S) fuzzy systems based on an event-triggered mechanism (ETM). An asynchronous approach is utilized to cope with the fuzzy-basis-dependent membership functions in networked fuzzy systems. For fear of the waste of communication resources, an ETM to screen the transmitted data is adopted for T-S fuzzy semiMarkov jump systems under consideration. Owing to the use of Lyapunov stability theory and some novel integral inequalities based on auxiliary functions, some sufficient conditions are obtained to guarantee that the resulting closed-loop system not only meets a prescribed mixed H∞and passive performance but also is stochastically stable. Then, by using a skillful matrix decoupling approach, the gains of the fuzzy controller may be expressed specifically. Lastly, the superiority and availability of the developed design method are extensively described and validated via its application to a truck control model.

In this article, a method to design the filter for fuzzy jumping genetic regulatory networks is explored. The case when the filters cannot directly utilize the mode information of the plant is taken into account. A hidden Markov model is introduced to address such a problem. Furthermore, a mature scheduling method, namely round-robin protocol, is employed to optimize the data transmission in genetic regulatory networks. On the basis of the fuzzy model approach and the stochastic analysis technique, some novel conditions ensuring the H∞performance and stochastic stability of the error system are established. The parameters of the filter can be presented via addressing the convex optimization problem. The feasibility of results is finally illustrated by considering a repressilator model subject to stochastic jumping parameters.

This paper studies the problem of synchronization for a class of Takagi–Sugeno (T–S) fuzzy complex networks, where the node dynamics may include partial coupling, diffusion coupling, discrete-time coupling, and delayed coupling. A fuzzy sampled-data control strategy that takes into account the time-delay effect is designed to solve the synchronization problem of such networks. Synchronization criteria are established for complex networks with the target node by constructing a modified time-dependent Lyapunov functional and using a mathematical induction approach. In contrast to most existing results, the constructed Lyapunov functional is neither necessarily positive on sampling intervals nor needlessly continuous at sampling instants. Moreover, a corresponding greater aperiodic sampling interval is obtained. Numerical simulation is presented to demonstrate the feasibility and validity of the proposed strategies.

This paper investigates the feedback guaranteed cost control problem for a class of uncertain discrete-time systems with input quantization. The plant with polytope type uncertainties is considered. The input signal will be quantized by a dynamic quantizer before it is transmitted from the controller to the system under digital communication channels. First, a design method of the feedback control for the quantized closed-loop system is given to attain the same guaranteed cost performance as the one without signal quantization. Then, by introducing some useful auxiliary scalars, the dynamic parameter of the quantizer is also designed effectively so that the desired performance level can be achieved. A simulation example is provided to show the effectiveness of the proposed design method.

This paper revisits the problem of stability analysis for neural networks with a time-varying delay. An improved general free-matrix-based (FMB) integral inequality is proposed with an undetermined number m . Compared with the conventional FMB ones, the improved inequality involves a much smaller number of free matrix variables. In particular, the improved FMB integral inequality is expressed in a concrete form for any value of m . By employing the new inequality with a properly constructed Lyapunov-Krasovskii functional, a new stability condition is derived for neural networks with a time-varying delay. Two commonly used numerical examples are given to show strong competitiveness of the proposed approach in both the conservatism and computation burdens.

The distributed dissipative state estimation issue of Markov jump genetic regulatory networks subject to round-robin scheduling is investigated in this paper. The system parameters randomly change in the light of a Markov chain. Each node in sensor networks communicates with its neighboring nodes in view of the prescribed network topology graph. The round-robin scheduling is employed to arrange the transmission order to lessen the likelihood of the occurrence of data collisions. The main goal of the work is to design a compatible distributed estimator to assure that the distributed error system is strictly (Λ1, Λ2, Λ3)y-stochastically dissipative. By applying the Lyapunov stability theory and a modified matrix decoupling way, sufficient conditions are derived by solving some convex optimization problems. An illustrative example is given to verify the validity of the provided method.

This paper investigates the issue of sampled-data stabilization for Takagi-Sugeno fuzzy memristive neural networks (FMNNs) with time-varying delay. First, the concerned FMNNs are transformed into the tractable fuzzy NNs based on the excitatory and inhibitory of memristive synaptic weights using a new convex combination technique. Meanwhile, a switched fuzzy sampled-data controller is employed for the first time to tackle stability problems related to FMNNs. Then, the novel stabilization criteria of the FMNNs are established using the fuzzy membership functions (FMFs)-dependent Lyapunov-Krasovskii functional. This sufficiently utilizes information from not only the delayed state and the actual sampling pattern but also the FMFs. Two simulation examples are presented to demonstrate the feasibility and validity of the proposed method.

This paper focuses on the design of a pinning sampled-data control mechanism for the exponential synchronization of directed coupled reaction-diffusion neural networks (CRDNNs) with sampled-data communications (SDCs). A new Lyapunov-Krasovskii functional (LKF) with some sampled-instant-dependent terms is presented, which can fully utilize the actual sampling information. Then, an inequality is first proposed, which effectively relaxes the restrictions of the positive definiteness of the constructed LKF. Based on the LKF and the inequality, sufficient conditions are derived to exponentially synchronize the directed CRDNNs with SDCs. The desired pinning sampled-data control gain is precisely obtained by solving some linear matrix inequalities (LMIs). Moreover, a less conservative exponential synchronization criterion is also established for directed coupled neural networks with SDCs. Finally, simulation results are provided to verify the effectiveness and merits of the theoretical results.

This paper studies the estimation problem for a class of nonlinear tunnel diode circuits with parameter perturbation. The dynamics of the nonlinear circuit is approximated by the Takagi-Sugeno fuzzy model with linear fractional parametric uncertainties. Considering the fuzzy filter with gain uncertainties, a filtering error system can be gotten naturally. The concentration of this paper lies in deriving the design conditions for the desired resilient filter such that the filtering error system preserves asymptotic stability as well as the L∞-norm of the transfer function from the external disturbance input to the filtering error output is below a specified bound. The filter design conditions for guaranteeing the prescribed peak-to-peak performance of the filtering error system are provided by a set of linear matrix inequalities. Finally, a simulation result is presented to illustrate the validity and effectiveness of the proposed filtering design for the parameter-controlled tunnel diode circuit.

In this paper, the quantized static output feedback dissipative tracking control problem is considered for a class of discrete-time nonlinear networked systems based on Takagi-Sugeno fuzzy model approach. The measurement output of the system, the output of the reference model, and the control input signals will be quantized by static quantizers before them being transmitted to the controller and the plant, respectively. The attention of this paper is focused on the design of the static output feedback tracking controller to asymptotically stabilize the nonlinear networked system and achieve strictly dissipative tracking performance subject to the quantization effects. Sufficient conditions for the existence of the static output feedback strictly dissipative tracking controller are expressed in terms of linear matrix inequalities. Two simulation examples are provided to show the effectiveness of the developed design method.

This paper mainly investigates the problem of extended dissipative filter design for switched discrete-time systems where the variation of switching signals among subsystems is governed by the persistent dwell-time strategy. Considering that in practical applications, the congestion of the communication channels between systems and filters may cause data loss, therefore the packet dropouts are taken into account to make the issue under consideration more general. Meanwhile, it is generally known that the mode information of the system may sometimes not be accessed, so we consider the design of a unified filter including mode-dependent and mode-independent filters simultaneously. A white sequence that obeys the Bernoulli distribution is used to express the random switching of the filters. The main purpose of this paper is to find a suitable filter design method which ensures that the resulting error system is exponentially mean-square stable and extended dissipative. Sufficient conditions are established to make sure the solvability of the addressed problem. By using appropriate decoupling method, some conditions that could be readily solved are obtained. In order to explain the correctness and effectiveness of the proposed method, two illustrated examples are given in the final part of this paper.

This paper investigates the exponential synchronization of coupled Lur'e dynamical networks with multiple time-varying delays and derivative coupling. In order to synchronize the Lur'e dynamical networks to the corresponding Lur'e systems, we propose a kind of impulsive pinning control strategy, where different functions of impulsive effects are taken into account. Sufficient conditions are derived for the exponential synchronization of the derivative coupled Lur'e dynamical networks by jointly applying the contradiction proof method, the concept of an average impulsive interval, comparison principle, and the extended parameters variation formula. Simultaneously, the convergence rates of exponential synchronization are obtained according to the definition of the impulsive solution equation on different functions of impulsive effects. Furthermore, three numerical examples are presented to demonstrate the validity of the theoretical analysis and the control protocol.

This paper addresses the adaptive fault-tolerant control (FTC) problem of uncertain switched nonaffine nonlinear systems with actuator failures and time delays. Unlike the literature employing the FTC methods, the novel nonlinear fault compensate function with adjustable parameter factor, whose norm is used for approximation with fuzzy logic systems, is constructed based on the mean value theorem. Accordingly, a standard transformation scheme from the multi-input and single-output nonaffine nonlinear system to the corresponding affine nonlinear system is first to be given; then, the designed adaptation mechanism with state-dependent switching strategy can effectively eliminate the effects of multiple time delays and actuator faults, including outage and stuck modes. Furthermore, by using the Lyapunov-Krasovskii stability analysis method, it is proved that the proposed adaptive switched controller can guarantee the states of the overall closed-loop systems to asymptotically converge to zero. Finally, the simulation results are displayed to verify the feasibility of the presented approach.

This paper addresses the problem of reducing the conservatism for stabilization of discrete-time Takagi-Sugeno fuzzy systems via a new extended representation approach. Different from previous slack variable methods, an advanced slack variable method without limitations is proposed for further making good use of available fuzzy weighting functions. Owing to the introduction of extended representations for homogenous polynomials, those strong limitations in recent literature are relaxed and thus the primary and secondary sequences of all normalized fuzzy weighting functions can be mapped to one augmented multi-indexed matrix for each homogeneous polynomial. Therefore, less conservative stabilization conditions can be provided as a result of these effective measures and thus a part of the online computational cost is transferred to the offline implementation. Finally, the benefits of the proposed method are illustrated by means of numerical experiments.

In this paper, we propose a constrained H∞ controller design method for the active suspension systems (ASS) with aperiodic sampling. The ASS are controlled by a hydraulic actuator which is usually limited by physical constraints. To utilize the information of saturation level, we express the ASS as the linear parameter varying (LPV) system by adopting time-varying parameters which depend on the constrained input. For the implementation of digital controllers, we consider the sampled-data H∞control. The stabilization condition is derived by using looped functional approach. The proposed controller guarantees the desired performance of the ASS despite of aperiodic sampling and input saturation. To demonstrate the validity of the proposed method, the simulation results are compared with the case of passive suspension systems.

This paper is concerned with the stability problem for neural networks with a time-varying delay. First, an improved generalized free-weighting-matrix integral inequality is proposed, which encompasses the conventional one as a special case. Second, an improved Lyapunov-Krasovskii functional is constructed that contains two complement triple-integral functionals. Third, based on the improved techniques, a new stability condition is derived for neural networks with a time-varying delay. Finally, two widely used numerical examples are given to demonstrate that the proposed stability condition is very competitive in both conservatism and complexity.

This paper investigates the exponential synchronization of nonidentically coupled Lur'e dynamical networks with proportional delay. Since the heterogeneities existed in different Lur'e systems, quasi-synchronization rather than complete synchronization is thus discussed. Different from general time delay, the proportional delay is a type of unbounded time-varying delay, which tremendously increases the requirements on network synchronization. Based on distributed impulsive pinning control protocol and different roles that impulsive effects play, the criteria for quasi-synchronization of nonidentically coupled Lur'e dynamical networks are derived by jointly applying the delayed impulsive comparison principle, the extended formula for the variation of parameters, and the definition of an average impulsive interval. Moreover, synchronization errors for different impulsive effects with different functions are evaluated and simultaneously, the corresponding exponential convergence rates are obtained. In addition, three numerical examples are presented to illustrate the validity of the control scheme and the theoretical analysis.

This paper is concerned with the problem of stabilization of chaotic systems via nonfragile fuzzy proportional retarded sampled-data control. Compared with existing sampled-data control schemes, a more practical nonfragile fuzzy proportional retarded sampled-data controller is designed, which involves not only a signal transmission delay but also uncertainties. Based on the Wirtinger inequality, a new discontinuous Lyapunov-Krasovskii functional (LKF), namely, Wirtinger-inequality-based time-dependent discontinuous (WIBTDD) LKF, is the first time to be proposed for sampled-data systems. With the WIBTDD LKF approach and employing the developed estimation technique, a less conservative stabilization criterion is established. The desired fuzzy proportional retarded sampled-data controller can be obtained by solving a set of linear matrix inequalities. Finally, numerical examples are given to demonstrate the effectiveness and advantages of the proposed results.

This paper focuses on the problem of dissipative filtering for nonlinear interconnected systems with interval time-varying delays. The considered nonlinear interconnected system is modeled by Takagi-Sugeno fuzzy rules. By constructing the delay dependent Lyapunov-Krasovskii functional and using new integral inequality, the delay-dependent condition is established to ensure that the derived closed-loop system is asymptotically stable with strict (Q, S, R) - α- dissipativity. In addition, a suitable filter is designed by solving a set of linear matrix inequalities. The presented method can provide better performance than the existing ones for the case of H∞ filtering. A simulation example is given to demonstrate the validity of the developed filter design technique.

This paper studies the problems of stability and stabilization of a class of chaotic systems (CSs) with Takagi-Sugeno fuzzy model and nonuniform sampling. It designs a fuzzy sampled-data protocol based on a switched idea to tackle the stability issue of such systems. Novel criteria on stabilization of CSs are established by employing a fuzzy membership function (FMF)-dependent Lyapunov functional and the information of the time derivative of FMFs, which significantly utilize the available characteristics of the actual sampling pattern and FMFs simultaneously. Unlike the existing works, a larger sampling interval is obtained by this new approach. A simulation example on chaotic Rossler's system is employed to demonstrate the superiority and reduced conservatism of the proposed method.

This paper focuses on the synchronization problem of Takagi-Sugeno (T-S) fuzzy complex networks with partial and discrete-time couplings via event-triggered sampling control. Different from traditional control methods, a more general and practical event-triggered communication scheme with nonuniform sampling is newly designed for T-S fuzzy complex networks. Then, a Lyapunov-Krasovskii functional (LKF) with a novel input-delay-product-type (IDPT) term is presented. The IDPT term can fully capture the information of the nonlinear functions and the actual sampling pattern. Based on the new IDPT LKF, less conservative synchronization criteria are derived. Meanwhile, by solving a set of linear matrix inequalities, the desired pinning control gains are precisely obtained. Simulation examples are provided to illustrate the effectiveness and superiorities of the proposed results.

This paper investigates the stability problem of Markovian neural networks (MNNs) with time delay. First, to reflect more realistic behaviors, more generalized transition rates are considered for MNNs, where all transition rates of some jumping modes are completely unknown. Second, a new approach, namely time-delay-dependent-matrix (TDDM) approach, is proposed for the first time. The TDDM approach is associated with both time delay and its time derivative. Thus, the TDDM approach can fully capture the information of time delay and would play a key role in deriving less conservative results. Third, based on the TDDM approach and applying Wirtinger's inequality and improved reciprocally convex inequality, stability criteria are derived. In comparison with some existing results, our results are not only less conservative but also involve lower calculation complexity. Finally, numerical examples are provided to show the effectiveness and advantages of the proposed results.

This paper focuses on the hybrid-driven-based H∞control for networked cascade control systems (NCCSs) with actuator saturations and stochastic cyber attacks. In order to relieve the network bandwidth load effectively, a hybrid triggered scheme is introduced, which contains a switch between time-triggered scheme and event-triggered scheme. A newly hybrid-driven-based NCCS model is established by considering the effects of both actuator saturations and stochastic cyber attack, which is an important threat to network security. By using the Lyapunov stability theory, sufficient conditions are obtained to ensure the system stability. Furthermore, both primary controller gain and secondary controller gain are achieved explicitly in terms of linear matrix inequality techniques. Finally, a power plant gas-turbine system is presented to illustrate the usefulness of the designed state feedback controllers.

This paper focuses on the problem of exponential stability for a class of neural networks with time-varying delays. A more general inequality is established which extends the auxiliary function-based integral inequality. Based on the inequality and parameter-dependent matrix inequality, an improved delaydependent stability criterion is obtained by constructing an augmented Lyapunov functional. Three numerical examples are given to illustrate the efficiency of the method.

In this paper, we develop robust adaptive nonsingular terminal sliding mode (NTSM) control methodologies to solve the position and the velocity tracking control problem of the automatic train operation (ATO) system subject to unknown parameters, model uncertainty, and external disturbances. A novel nonlinear nonsingular terminal sliding manifold is proposed by considering that its parameter is unknown, which need to be estimated via a proposed non-negative adaptive law. And a corresponding novel robust adaptive NTSM control strategy, which enables the position tracking error and the velocity tracking error of the ATO system to converge to zero, and eliminates the singularity caused by terminal sliding mode controller, is proposed. Furthermore, unknown parameters of the sliding manifold and the ATO system can be estimated online by the proposed methodology. Simulation results show the effectiveness of the proposed methodologies in this paper.

This paper investigates the problem of sampled-data (SD) exponentially synchronization for a class of Markovian neural networks with time-varying delayed signals. Based on the tunable parameter and convex combination computational method, a new approach named flexible terminal approach is proposed to reduce the conservatism of delay-dependent synchronization criteria. The SD subject to stochastic sampling period is introduced to exhibit the general phenomena of reality. Novel exponential synchronization criterion are derived by utilizing uniform Lyapunov-Krasovskii functional and suitable integral inequality. Finally, numerical examples are provided to show the usefulness and advantages of the proposed design procedure.

Partial state estimation has numerous applications in practice. Nevertheless, designing delay-dependent functional observers (FOs) for systems with unknown time delays is rigorous and still an open dilemma. This paper addresses the problem for linear time-invariant systems with state time-varying delays. The delay is assumed to be bounded in an interval with a bounded derivative. A sliding mode FO structure that is robust against the delay uncertainties is established to this aim. The structure employs an auxiliary delay function that can be defined based on the existing knowledge on the actual delay values. Delay-dependent sufficient conditions for the stability of the observer are obtained using the Lyapunov Krasovskii approach, and are expressed in terms of a linear matrix inequality and two rank conditions. The delay-free observer structure is additionally studied and the necessary and sufficient conditions for its stability are obtained. Two descriptive numerical examples and simulation results demonstrate the design procedure and emphasize the effectiveness of the proposed observer design algorithm.

This paper investigates the problem of event-triggered control for a class of fuzzy Markov jump systems with general switching policies. A novel event-triggered scheme is proposed to improve the transmission efficiency at each sampling instance. Each transition rate allows to be unknown, known, or only its uncertain domains value is known. With the help of a tailored technique to bind the uncertain terms and an asynchronous operation approach to tackle the fuzzy system and fuzzy controller, sufficient conditions for the resulting fuzzy Markovian jump systems are established in terms of coupled linear matrix inequalities. Finally, an example is given to illustrate the validity of the developed technique.

In this paper, event-triggered reliable dissipative filtering is investigated for a class of Takagi-Sugeno (T-S) fuzzy systems. First, a reliable event-triggered communication scheme is introduced to release sampled measurement outputs only if the variation of the sampled vector exceeds a prescribed threshold condition. Second, an asynchronous premise reconstruct method for T-S fuzzy systems is presented, which relaxes the assumption of the prior work that the premises of the plant and the filter are synchronous. Third, the resulting filtering error system is modeled under consideration of event-triggered communication, sensor failure, and asynchronous premise in a unified framework. By adopting the Lyapunov functional method and integral inequality approach, a delay-dependent criterion is developed to guarantee asymptotic stability for the filtering error systems and achieve strict (Q, S, R) - α dissipativity. Consequently, suitable filters and the event parameters can be derived by solving a set of linear matrix inequalities. Finally, an example is given to show the effectiveness of the proposed method.

This note investigates sampled-data control for chaotic systems. A memory sampled-data control scheme that involves a constant signal transmission delay is employed for the first time to tackle the stabilization problem for Takagi-Sugeno fuzzy systems. The advantage of the constructed Lyapunov functional lies in the fact that it is neither necessarily positive on sampling intervals nor necessarily continuous at sampling instants. By introducing a modified Lyapunov functional that involves the state of a constant signal transmission delay, a delay-dependent stability criterion is derived so that the closed-loop system is asymptotically stable. The desired sampled-data controller can be achieved by solving a set of linear matrix inequalities. Compared with the existing results, a larger sampling period is obtained by this new approach. A simulation example is presented to illustrate the effectiveness and conservatism reduction of the proposed scheme.