In this paper, we study the data-drivenH∞ optimal control problem for output synchronization of heterogeneous multi-agent systems via a computationally efficient reinforcement learning (RL) method. Unlike the existing RL control results which solve for the optimal controller through the vectorization and Kronecker product (VKP) operations where the large size of the Kronecker product increases computational complexity, a computationally efficient data-driven RL control method is studied in this paper. First, the optimal output synchronization problem is transformed into anH∞ optimal control problem by constructing an internal model-based distributed control protocol. Then, the corresponding algebraic Riccati equation for theH∞ optimal control is reformulated as a generalized Sylvester-transpose matrix equation (GSTME) at each iteration. To solve the GSTME, thematrixform of the conjugate gradient least squares algorithms that do not require the VKP operations is proposed based on the system dynamics and measurement data of multi-agent systems, respectively. Furthermore, the initial stabilizing gain required for policy iteration learning is proposed based on a data-based solution method. Finally, the feasibility of the designed algorithms is demonstrated through a numerical simulation, and its better computational efficiency compared to existing methods is shown.

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.

This work studies the decentralized sliding mode control problem for interconnected systems based on a dynamic event-triggered transmission mechanism. To model unreliable communication networks that are susceptible to both energy-limited denial-of-service (DoS) attacks and transmission failures, a Markov model is adopted. A decentralized dynamic event-triggered mechanism is introduced to efficiently utilize limited channel resources by regulating data transmission based on system measurement outputs. Moreover, a hidden Markov model is used to estimate the working operation of the network transmission channel such that a hidden Markov model-based sliding mode controller can be designed by using the output feedback control method. By constructing a Lyapunov function, sufficient conditions are deduced to ensure the stochastic stability of the system and the reachability of the specified sliding mode surface, and a solvable criterion for obtaining the hidden Markov model-based sliding mode controller gains is presented. Finally, simulation results of a four-area interconnected power system are presented to demonstrate the effectiveness of the proposed method.

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 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 addresses the problem of encryption-decryption-based bipartite synchronization control for a class of discrete-time coupled neural networks, in which the nodes exhibit both cooperative and antagonistic interactions. Initially, a Markov chain with concealed operating modes is employed to describe Markov jump coupled neural networks with switching topologies. In this framework, a hidden Markov model is incorporated, whose emission values express the system mode. Next, the decentralized adaptive event-triggered strategy is proposed to alleviate the communication burden caused by interactions between nodes. Moreover, an encryption-decryption algorithm that takes into account identity authentication is programmed to encrypt the data at the triggering moment of each node, thereby securing the data interaction privacy. Then, the observation mode-based bipartite synchronization control law is formulated to fulfill the control demands of the plant. Furthermore, some sufficient conditions for the networks to be mean square synchronized and satisfy theH∞performance are obtained based on the Lyapunov stability theory. At last, two simulation examples involving chaotic neural networks are presented to verify the effectiveness of the proposed method.

In this paper, a novel hidden semi-Markov model (HS-MM) in the continuous-time domain is proposed, and theH∞static output feedback control problem for hidden semi-Markov jump linear systems (HS-MJLSs) is investigated. Data transmission via a wireless network medium in HS-MJLSs is vulnerable to cyber attacks. Therefore, we adopt a resilient control strategy to ensure the stability of HS-MJLSs under hybrid cyber attacks, factoring in both denial-of-service attacks and false data injection attacks. In comparison with the conventional Markov jump linear systems, the HS-MM proposed by us is more universal, which is reflected in the following two aspects: (i) The system mode is considered to be undetectable. The jumping of the controller depends on the observation mode, and is asynchronous with the jumping of the system mode. (ii) Both the mode jumps of the original system and controller are considered to be related to the sojourn-time, which is more reasonable than the memoryless jumping processes. By using the established HS-MM and a Lyapunov-based approach, some sufficient conditions are obtained to ensure the stochastic stability of the HS-MJLSs with anH∞performance and hybrid cyber attacks. Finally, the proposed theoretical results are demonstrated by two simulation examples.

This paper investigates the control problem of two-dimensional nonlinear semi-Markov jump systems with saturation constraints in the Roesser model. To address the nonlinearity of the system, the Takagi–Sugeno fuzzy model is employed, and a less conservative approach in handling the mismatched double fuzzy summation inequality is adopted, which removes the introduction of additional decision variables. Moreover, a novel mode generation method is employed, ensuring that the system mode index depends only on the sum of variables in two directions. In this case, the mode ambiguity problem in traditional methods is solved effectively. Furthermore, considering that the control input of a real two-dimensional process is limited by the physical capabilities of the actuator, actuator saturation is incorporated into the controller design within the proposed fuzzy framework. Then, by introducing concepts from the switching system and based on fuzzy model methods and Lyapunov theory, an almost inevitable exponential stability condition for the two-dimensional semi-Markov jump fuzzy nonlinear systems is established. By solving the optimization problem under the established stability condition, the attraction region is estimated. Finally, the effectiveness of the designed controller is verified through a numerical example and an example of heat exchange between a heated fluid inside a pipe.

This paper investigates the exponential synchronization of reaction-diffusion systems with communication delays and non-collocated sensor-actuator deployments. To mitigate the communication resource wastage inherent in traditional continuous-time control, a dwell-time-based event-triggered scheme is proposed, which effectively reduces the frequency of control signal updates. A limited number of sensors are spatially deployed to measure local average signals, and only valuable data are transmitted through the proposed event-triggered mechanism. To address communication delays induced by bandwidth constraints and locally distributed actuators, a spatiotemporal-dependent switching system is constructed. Subsequently, sufficient conditions for ensuring exponential synchronization are derived by constructing appropriate Lyapunov functionals that accommodate both collocated and non-collocated sensor-actuator configurations. Finally, numerical simulations and an image encryption application validate the effectiveness of the proposed approach. The results demonstrate that the method improves delay tolerance by allowing a larger sensor distribution ratio without modifying actuator placement, offering practical insights for the design of efficient and robust networked control systems.

Robust multiobjective evolutionary algorithms (RMOEAs) aim to obtain robust optimal solutions. However, traditional RMOEAs typically require evaluating a large number of sampling points, which is often impractical in real-world applications due to the high computational cost. In this article, we propose a robust multiobjective evolutionary algorithm based on surrogate-assisted (RMOEA-SA), which incorporates a radial basis function (RBF) surrogate model and a novel robust distance metric (RDM). The proposed algorithm employs the RBF surrogate model to approximate the fitness values of sampling points, thereby significantly reducing the number of function evaluations during the robust optimization process. Furthermore, an RDM assisted by the RBF surrogate model is introduced to measure the robustness of solutions. Besides, the RDM value of each solution is treated as an additional objective, expanding the original objective space, and selection is conducted in this augmented space to achieve a desirable trade-off between robustness and optimality. The experimental results on standard benchmark functions and two real-world application problems demonstrate the superior feasibility and effectiveness of the proposed method compared with several existing algorithms.

This article presents a data-driven control method to address the local asymptotic stabilization problem of discrete-time neural networks (DNNs) under input saturation. To reduce communication load, a memory-type event-triggered mechanism (MEM) is first designed to mitigate the superfluous triggers. Then, a memory-dependent Lyapunov function (MLF) is constructed to accommodate the memory term introduced by the MEM. Based on the designed MEM, the MLF and two data-based system representations, a data-based stabilization criterion is developed, and an estimated region of attraction (ERA) is determined. Simultaneously, the feedback gain and the trigger matrix are co-designed to guarantee the local stability of the closed-loop system. A notable feature of the proposed approach is that the proposed stabilization criteria rely solely on accessible data, without necessitating full knowledge of the system matrices. It makes the approach well-suited for practical applications where precise modeling is difficult or infeasible. Furthermore, a hybrid optimization scheme combining the linear objective minimization method and the particle swarm optimization (PSO) algorithm is presented to maximize the size of the ERA. Finally, two numerical simulations are given to validate the effectiveness of the proposed optimization algorithm, illustrate the influence of data size, and demonstrate the advantages of the designed MEM in stabilizing DNNs.

This article proposes a novel reinforcement learning (RL)-based secure control policy for nonlinear Markov jump systems (MJSs) subject to false data injection attacks (FDIAs). First, the Takagi-Sugeno (T-S) fuzzy model is applied to describe the nonlinear MJS. A min-max strategy and an off-policy homotopic Q-learning (HQ) scheme are then introduced to design a secure control policy without requiring knowledge of the system dynamics. The proposed approach offers two main advantages: it does not require an initial stabilizing control gain, and it guarantees unbiased learning under persistently excited conditions. Furthermore, a rigorous stability analysis of the overall closed-loop system under FDIAs is presented. Finally, the effectiveness of the proposed approach is demonstrated using a tunnel diode circuit.

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.

Autonomous berthing is a critical step in realizing the full autonomy of unmanned surface vehicles (USVs), which can essentially be regarded as a trajectory-tracking task. It can be further transformed into a problem of nonlinear systems with output constraints. This paper proposes a novel adaptive fixed-time backstepping control scheme based on the barrier Lyapunov function (BLF) for autonomous berthing of USVs. Firstly, a new barrier Lyapunov function is designed to solve the output asymmetric constraint requirement of the autonomous berthing system, and it is also adaptive to the unconstrained system without changing the control structure. Secondly, the convergence of adaptive fixed-time control and bounded tracking of BLF are combined to conquer the long convergence time and nonlinear system uncertainty. Finally, simulation and field tests are conducted to verify our proposed scheme’s superiority.

This work considers the$H\_{\infty }$fuzzy control issue of 2-D fuzzy systems susceptible to hybrid cyberattacks due to the unreliable communication networks modeled by the Markov chain, in which the hybrid cyberattacks include deception attacks and denial-of-service attacks. Meanwhile, considering the concealment characteristics of cyberattacks, it may be impossible to know which type of attack the system is suffered. Therefore, a unified hidden Markov model is used in this work, in which the hidden mode represents the real attack mode, the observed mode is the estimated value of the attack mode and it can be directly utilized by the designed security controller. By utilizing the attack mode-dependent Lyapunov function, the asymptotically mean square stability of the 2-D resultant system can be ensured and a set of sufficient conditions can be obtained. Finally, two illustrative examples are provided to demonstrate the applicability of the obtained results.

This article addresses the issue of the event-triggered fixed-time consensus tracking control (ET-FT-CTC) for a class of strict-feedback nonlinear multiagent systems (NMASs) with unmeasurable states. The main challenges in these systems include partial unmeasurable states, completely unknown nonlinearities, and the occasional unavailability of the reference signal and its first-and second-order derivatives. To address these challenges, a nonlinear observer is designed to estimate the unmeasurable states, and a set of radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinearities. Additionally, a novel update law is introduced to manage the unavailability of the reference signal. The dynamic surface control (DSC) approach is utilized to reduce the computational burden associated with the backstepping method, while a set of novel virtual controllers is constructed to avoid singularity problems. The proposed adaptive ET controller ensures that the CTC objective is achieved within a fixed time, without the occurrence of Zeno behavior. Two illustrative examples are provided to demonstrate the effectiveness of the proposed method.

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.

This article concerns the problem of an event-triggered predefined-time neural tracking control for high-order nonlinear systems (HONSs) through a switching event-triggered strategy (ETS). In contrast to the previous research on HONSs, one prominent virtue of this work is that the upper bound of settling time can be determined a priori by a separate controller parameter. First, by employing the addition of a power integrator technique and a switching event-triggered rule, an adaptive event-triggered controller is developed under the backstepping framework. Therein, the unknown nonlinearities can be approximated effectively through neural networks (NNs). Unlike many existing approaches, the proposed control strategy is not only proficient in coping with the coupling term caused by the event-triggered rule and the nonlinear function, but also has excellent effects in saving communication resources. In terms of the predefined time stability theory, the boundedness of all variables and the fast convergence characteristics of the tracking error within the predetermined time in the system are guaranteed. Finally, the simulations are provided to validate the effectiveness of the suggested control scheme.

The design, simulation, and testing of intelligent trajectory-tracking control in narrow waters are essential issues for unmanned surface vehicles (USVs). Due to limited actuators, spatial constraints, and obstacles in narrow waters, the reference trajectory for USVs has various curves. This presents significant challenges to the accuracy and computational load of trajectory tracking. Therefore, a novel event-triggered-based nonlinear model predictive control (NMPC) with an artificial reference trajectory (ENMPC-ART) method is proposed. The artificial reference decision variables are integrated into the quadratic trajectory planning of reference trajectory and motion control of USVs to reduce the cross-track error. An event-triggered mechanism is designed to improve NMPC’s efficiency. Further, a cyber-physical simulation test framework based on virtual reality is designed to verify the algorithm’s performance and enhance the immersion. Finally, the proposed ENMPC-ART shows significant improvements through virtual simulations and field tests, such as the maximum cross-track error being reduced by 16% and the computation time being reduced by 20.2%.

Autonomous collision avoidance technology is the core of unmanned surface vehicles (USVs). Deep reinforcement learning (DRL) is a new approach to avoid collision for USVs. However, most research is based on the assumption of a fixed number of obstacles and ignores the collision prediction to improve safety. To address this problem, a novel “prediction-decision” collision avoidance model based on the deep deterministic policy gradient (DDPG) is proposed. First, a radiation-shaped state space is designed to make the DDPG that can be used in time-varying scenarios with stochastic obstacles. Then, the velocity obstacle (VO) is combined with the state space for training to realize the collision prediction. Subsequently, reward functions are designed using a reward-shaping technique to improve training efficiency and safety. Finally, virtual simulation experiments based on Unity3D and field tests are conducted to verify the algorithm’s performance. The results show that it can take safe collision avoidance actions in unknown environments and with generalization ability.

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.

This paper studies the event-triggered sliding mode control problem for interval type-2 fuzzy interconnected systems under hybrid cyberattacks modeled by Markov processes. Under the framework of an interval type-2 fuzzy model, the nonlinear relationship in the interconnected system is modeled and analyzed, and a fuzzy sliding mode controller is designed which can effectively resist hybrid cyberattacks. To rationally utilize limited channel resources, a decentralized event-triggered mechanism is used to reduce unnecessary information transmission. By constructing a Lyapunov function, some criteria are deduced to ensure that the closed-loop system is stochastically passive and the reachability of the designed sliding area is guaranteed. Finally, a four-area networked interconnected system is used to verify the effectiveness of the decentralized fuzzy sliding mode control strategy.

This paper raises a predefined-time event-triggered adaptive tracking controller for nonlinear systems with time delays. To mitigate the effects of time-varying delays, a Lyapunov-Krasovskii function is initially introduced. A set of adaptive laws and virtual controllers with switching functions are designed to avoid singularity phenomena. In addition, the event-triggered mechanism with the switching threshold can effectively conserve communication resources while avoiding the occurrence of Zeno behavior. It is theoretically demonstrated that, under the devised control strategy, the system output successfully tracks the reference signal with negligible tracking error within a specified time. Lastly, the validity of the raised control strategy is demonstrated through examples.

This paper investigates the optimal control problem for interconnected systems with unknown system dynamics through a two-stage reinforcement learning method. First, to address the impact of interconnection term, the optimal control problem for interconnected systems is transformed into obtaining the solution of game algebraic Riccati equations within the framework of$H_{\infty }$control method. Furthermore, existing optimal control approaches for interconnected systems necessitate precise knowledge of system dynamics, which is difficult to obtain accurately or involves high costs. Thus, we introduce a two-stage reinforcement learning method. The admissible control policies are obtained using the homotopy-based iteration method in the first stage. Then, the optimal control policies are obtained through the policy iteration method in the second stage. The two-stage method not only eliminates the requirement for system dynamics and initial admissible control policies but also ensures convergence speed and accuracy, significantly enhancing its practicality. Finally, a two-machine power system example is provided to validate the feasibility of the two-stage method. Note to Practitioners—Interconnected systems, a class of systems composed of multiple local subsystems, find wide applications in various fields such as power systems, transportation networks, and spatially interconnected systems. Particularly, the optimal control problem of interconnected systems has gradually become a focal point of current research. However, the current research on the optimal control problem of interconnected systems is still constrained by the system dynamics and the initial stability of the system. To relax these limitations, this paper introduces a two-stage method. A homotopy-based iteration approach is employed to obtain control policy and interconnection policy that make the system closed-loop stable, thus achieving the optimal solution. Furthermore, the data-driven approach overcomes the limitations imposed by system dynamics. The feasibility of the two-stage method is illustrated by a two-machine power system model.

This article studies multisynchronization of coupled multistable neural networks (NNs) with directed topology via event-triggered impulsive (ETI) control. At first, the activation function (AF) with${2p}$corners is proposed and it is proved that an n-neuron subnetwork can produce${(p+1)^{n}}$locally stable equilibrium points (EPs) or periodic orbits (POs) under some criteria. Furthermore, to achieve multisynchronization, an ETI controller is designed. Compared with conventional impulsive control strategy, ETI control strategy proposed in this paper can reduce the communication cost and save the bandwidth. Sufficient conditions are given to ensure both dynamical multisynchronization (DMS) and static multisynchronization (SMS) of coupled neural networks (CNNs) with fixed and switching topologies. Moreover, it is proved that the Zeno behavior can be avoided. Lastly, two examples and the application to associative memory are illustrated to testify the validity of the obtained results. Note to Practitioners—ETI control could reduce the number of packets sent as well as the control cost compared with conventional impulsive control. In addition, combining impulsive control with event-triggered schemes into multisynchronization analysis of CNNs is challenging because of the large number of synchronization manifolds in CNNs. Therefore, this research studies multisynchronization of coupled multistable NNs with directed topology via ETI control. A kind of ETI controller is designed and Zeno behavior is avoided. Moreover, the multisynchronization of CNNs is applied to the associative memory for the first time. Compared with the multistability-based associative memory, the multisynchronization-based associative memory can have superiority in resisting the noise interference.

2-D systems which can model many practical engineering systems, have attractive research in the control topic in both practice and theoretical aspects. Due to the complex engineering environment, systems may encounter sudden structural changes and be unable to work properly. Fortunately, the Markov jump process can model this situation. Meanwhile, the information of systems may not be accessed timely or only partial information can be accessed. This work focuses on the hidden Markov model-based sliding mode control for 2-D Markov jump systems with partially unknown information and its application in the metal rolling process. By utilizing the Lyapunov function approach, some criteria are obtained to guarantee that the dynamics state reaches the designed sliding surface within a finite time, and the system is passive. Finally, the practicability of asynchronous sliding mode control law is verified by providing a metal rolling process. Note to Practitioners—In some practical systems, their state depends on two independent variables, and the internal structure or parameters of the system are changed due to sudden environmental interference and device failure, which can be described as 2-D Markov jump systems. Meanwhile, in practical engineering applications, obtaining all statistical probability information of systems requires a great cost or is impossible to accomplish. This paper proposes the sliding mode control method for 2-D Markov jump Roesser systems with partial information, in which the partial information problems include the partially known system operation mode information and the partially known probabilities information. A hidden Markov model is used to address the above two partial information problems. The design approach of a hidden Markov model-based sliding mode controller with partially unknown statistical probability information is proposed. The engineering applicability of the method is verified by the metal rolling technology. This study provides a new sliding mode control method to address the control problem for 2-D systems where the system information is partially obtained or accessed.

This paper proposes a data-based reinforcement Q-learning control method to efficiently solve the$H_{\infty }$control problem for discrete-time double-layer Markov jump systems (DLMJSs). The transition probabilities are considered to be piecewise-homogeneous and governed by a persistent dwell-time (PDT) switching scheme. First, a model-based stability and performance optimization criterion for DLMJSs is derived. On this basis, a novel reinforcement Q-learning method in the form of linear matrix inequality is presented, such that optimal disturbance attenuation level and$H_{\infty }$control policy can be calculated from the collected data. At the core of the approach is a two-step learning design associated with the Q-function. The first step is to estimate the kernel matrix by solving the minimization problem. The second step is to find an improved control policy based on the estimated kernel matrix. Furthermore, the rigorous analysis of the stability of closed-loop DLMJSs and the convergence of the designed algorithm are presented, ensuring that closed-loop systems achieve mean-square exponential stability with an optimal disturbance attenuation level. Finally, the applicability and effectiveness of the proposed method in engineering applications are verified by an armature controlled DC motor system model. Note to Practitioners—Due to the complexity of engineering environment, the$H_{\infty }$control of DLMJSs has garnered extensive attention. Nowadays, DLMJSs find widespread application in various domains including tidal turbine systems, robot manipulators, and smart grids. It should be noted that existing$H_{\infty }$control methods for DLMJSs typically necessitate precise system dynamics and assume a prescribed disturbance attenuation level. However, in practical applications, these assumptions are difficult to satisfy. In view of these facts, we employ the reinforcement Q-learning control method to solve the$H_{\infty }$control problem of unknown DLMJSs with PDT-switched transition probabilities. Moreover, the designed control method can achieve both the optimal disturbance attenuation level and the optimal$H_{\infty }$control policy of the studied systems, providing a feasible strategy for improving control performance in industrial applications.

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 investigates the adaptive event-triggered data-driven control problem for a class of unknown nonlinear discrete networked systems. To address this problem, a stochastic configuration network-based algorithm is developed to construct a candidate mapping set within the modeling-valid domain. Subsequently, an adaptive event-triggered control protocol is proposed, and a closed-loop mapping set is obtained. Then, by leveraging the ideas of transfer stabilization and the S-lemma, a data-driven stability criterion for nonlinear discrete networked systems is derived. The stability criterion solely relies on the data of the controlled system and is independent of both the system model and the data model. Based on this stability criterion, the control gain and triggering matrix of the controlled system can be obtained. Additionally, the effectiveness and practicality of the proposed method are validated through a numerical example and a complex memristive Hopfield neural network circuit.

This paper investigates the bipartite synchronization problem of cooperative-antagonistic neural networks (CANNs) that suffer from hybrid attacks, i.e., denial-of-service (DoS) attacks and replay attacks. A resilient sampled-data control scheme is proposed to deal with the hybrid attacks. In addition, the Laplacian matrix with zero-row-sum is obtained based on the coordinate transformation method. On this basis, an easy-to-handle error system is constructed by integrating the sampling scheme, cooperative-antagonistic interactions and hybrid attacks. Subsequently, an interval-dependent function is designed by considering the characteristics of both replay and DoS attacks. Based on this, using Lyapunov function methods, inequality techniques, and other mathematical skills, some sufficient criteria are obtained for the bipartite synchronization of CANNs. Finally, three algorithms are proposed to minimize the coupling strength, or maximize the replay attack rate or the DoS attack rate, respectively. The effectiveness of the proposed control schemes and the advantages of the interval-dependent function are verified through numerical examples.Note to Practitioners—This work addresses the bipartite synchronization control problem of CANNs, which can be applied to some practical systems such as drone bidirectional formation and team competition. For example, the dynamics of drones within the same formation, including their velocity and position, are consistent with each other but opposite to those of other teams. Additionally, network systems across various industries often encounter DoS attacks or replay attacks during network transmission. A resilient sampled-data control scheme is proposed, and a Lyapunov function dependent on intervals is designed,taking into account the characteristics of the attack. Then, some algorithms are presented to minimize the allowable coupling strength or maximizing the allowable replay attack rate and DoS attack rate. Preliminary simulation experiments indicate that this sampling scheme is feasible. The types of network attacks are diverse. Therefore, obtaining system synchronization control schemes for more complex network attacks is a challenge.

This paper deals with the bipartite synchronization problem of cooperation-competition neural networks (CCNNs) subject to denial-of-service (DoS) attacks. A resilient sampled-data control strategy is proposed to mitigate the adverse impact of DoS attacks, which takes both the attack signal and the periodic sampling communication protocol into account. The directed signed graph is introduced to characterize the cooperation and competition interactions among nodes. By leveraging coordinate transformation and graph theory techniques, a zero-row-sum Laplacian matrix is constructed to facilitate subsequent analysis. In combination with DoS attacks and control strategies, a tractable error system model is formulated. An interval-dependent function is further introduced, taking into account both attack intervals and data transmission intervals. Based on Lyapunov stability theory, the convex combination approach, and inequality techniques, the bipartite synchronization criteria for CCNNs are obtained. Moreover, the constructed interval-dependent function can improve the maximum allowable attack rate or reduce the minimum allowable coupling strength. The proposed control scheme is demonstrated to be effective and superior through the two numerical examples.

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 article focus on the quasi-multisynchronization and quasi-monosynchronization of delayed neural networks (DNNs) with parameter mismatch via impulsive control. At first, a kind of actication functions (AFs) is proposed and multistability/monostability criteria of DNNs are given. By judging 2nalgebraic inequalities, then-neuron DNNs with this kind of AFs can produce 2nlocally stable equilibrium points (EPs) or one globally stable EP. In addition, a kind of impulsive controller is designed. Impulsive control strategy proposed in this paper can save the bandwidth and reduce the communication cost compared with continuous-time control strategy. Moreover, the concept of quasi-multisynchronization of DNNs is proposed for the first time and sufficient conditions are given to ensure quasi-multisynchronization and quasi-monosynchronization of DNNs by building the comparison system and using the Lagrange method of variation of parameters. Under this control method, a monostable system can become a multistable system, which means that the storage capacity (SC) of DNNs is improved. Lastly, two examples are illustrated to testify the validity of the proposed theory and method.

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.

Hydraulic servo systems are generally affected by disturbances, parametric uncertainties, and unmodeled dynamics. In order to improve the tracking performance of hydraulic servo systems, a new adaptive output feedback funnel control method with uncertainty compensation is proposed based on dynamic surface technology, which avoids the differential explosion problem generated in backstepping control and keeps the tracking performance always within a predetermined range. The observers and adaptive control are fused together to compensate for system model uncertainties and estimate unknown states; Meanwhile, a new control error variable with finite-time performance function is used to constrain system performance. The closed-loop system using this controller has high accuracy and strong robustness. It has been proven that the estimation errors are bounded and the control error of the closed-loop system is kept within the bound of funnel function. Experiments were conducted to test the high-performance properties of the proposed algorithm. Note to Practitioners—Hydraulic servo systems have received increasing research attention in recent years because of their widespread range of applications. In this paper, the high transient performance and precise steady-state tracking control of hydraulic servo system are studied. We propose a new adaptive output feedback funnel control method with uncertainty compensation based on dynamic surface technology. the associated stability proof is then presented. Experiments are conducted to demonstrate the effectiveness of the approach. In future research, we will address the problem of high precision control of hydraulic manipulator.

The canal waterways of China still play an important role in the logistics and transportation industry. Unmanned technology helps to reduce costs and improve the safety of navigation. Real-time environmental awareness is vital to making unmanned surface vehicles (USVs) come true. This paper proposes a new lightweight environmental awareness method based on deep convolutional neural networks (DCNN) and a mixed attention mechanism for USVs in canals, which can simultaneously perform ship detection, segmentation, and surface and background segmentation tasks. The features of the ships, surface, and background are extracted by a shared feature extraction backbone network and hybrid attention mechanism, which improves the efficiency of visual environmental awareness. In addition, a dataset namedUSV-Canalis constructed to enrich the features of canal waterways for environmental awareness, which contains typical canal scenes and 3443 ship objects. To improve the generalization, multiple public datasets are mixed with theUSV-Canaldataset to build an integrated dataset to train our model, which boasts diversity in scene types and ship classes. The comparative and field experiments’ results show that 40.9% ofmAP, 95.8% ofmIoU,and 5 frames per second (FPS) inference speed can be achieved, and have good generalization, which can meet the requirements of environmental awareness of low-speed ships in canal waterwaysNote to Practitioners—The trained and validated model can ultimately be deployed on unmanned surface vehicles, and the required hardware platform is NVIDIA’s Jetson Nano, which is used for real-time perception of surrounding ships and navigable surfaces during navigation. The information can be integrated into the guidance, navigation, and control (GNC) system of USVs, achieving obstacle avoidance and ensuring safe navigation. It is vital to make autonomous navigation come true.

This paper addresses the predefined-time tracking control problem for nonlinear systems subject to predetermined performance metrics and input saturation. With the assistance of the reinforcement learning method, the optimization control objectives of nonlinear systems are achieved. Therein, the execution costs and control behaviors are trained effectively under the actor-critic neural network framework. In addition, the unknown nonlinearities and external disturbances are delicately compensated through the neural approximation rule and an adaptive updated law, respectively. To meet the performance requirements, a performance function is designated to acquire the satisfaction of performance metrics. By incorporating an auxiliary control signal, the saturation constraint on control input is satisfied. In particular, under the action of the developed scheme, the boundness of each system state can be ensured in a predefined time interval. Finally, the effectiveness and superiority of the developed scheme are demonstrated via simulation results. Note to Practitioners—This paper aims to achieve the optimized predefined-time tracking control for nonlinear systems with input saturation and performance guarantees. With the improvement of automation, predefined time convergence becomes one significant performance index in many time-critical practical projects, such as teleoperation systems, missile guidance systems, and so on. As a matter of fact, since actuator saturation and external disturbances exist extensively in practice, they constantly impair the operation performance of engineering systems. To deal with the aforementioned challenges, a reinforcement learning-based predefined-time anti-saturation controller is designed in this paper, which ensures that the system converges within a user-specific time while minimizing the cost. Preliminary simulation comparisons demonstrate that this developed scheme applies to practical servo systems with satisfactory control results.

In this paper, a data-based containment control method is proposed for multi-agent systems where the system parameters of both leaders and followers areheterogeneousandunknown. Based on the collected data from the leader systems, the least square method is first utilized to identify the parameters of the leaders. Then, the data-based adaptive distributed compensator is designed for each follower to estimate the system parameters and the states of theheterogeneousleaders. Meanwhile, the collected data from follower systems is used to construct the local observer and establish the data-based linear matrix inequalities to determine the observer gains and feedback gains. On the basis of the collected data from leader and follower systems, the data-based adaptive solutions to the output regulation equations are provided. Furthermore, the data-based containment control protocols are designed to realize that the outputs of the followers converge to the convex hull formed by the unknown heterogeneous leaders. Finally, the effectiveness of the proposed data-based control scheme is illustrated by the simulation of the numerical example and the interconnecting RLC circuits.

This article proposes a novel scalable control strategy for large-scale direct current microgrids (LSDCmGs) consisting of multiple distributed generation units (DGUs). The objective is to facilitate real-time plug-and-play (PnP) functionality for DGUs, despite the presence of power transmission line coupling. In brief, the insertion or removal of DGUs does not require reconfiguration of adjacent controllers to maintain the stability of the entire LSDCmGs. To achieve this goal, a generalized free-weighting matrix technology is introduced. The strict assumptions previously made on Lyapunov matrices are ingeniously shifted to free-weighting matrices by this technology. As a result, scalable control is achieved and conservatism is reduced simultaneously. Moreover, the topological structure of LSDCmGs frequently undergoes changes due to external factors, equipment failures, and line issue. Markov chains are employed to accurately model these variations, providing a more realistic analysis of the microgrid's operational states. Additionally, by selecting a mode-related structured Lyapunov-Krasovskii function, sufficient criteria are derived to realize tracking of a given reference voltage in the LSDCmGs under PnP operation. Finally, the effectiveness of the proposed scalable strategy is validated through a case study involving the LSDCmGs with five DGUs.

The bipartite consensus (BC) issue for nonlinear multiagent systems (NMASs) with unknown system dynamics information is investigated in this article. Initially, the dynamics of NMASs are represented using the Takagi-Sugeno (T-S) fuzzy model. Subsequently, to achieve distributed control, a minmax game policy is introduced, where each agent aims to minimize its performance index while its neighbors attempt to maximize it. Consequently, the BC problem for NMASs is reformulated as a zero-sum game, transforming the controller design into solving a set of game algebraic Riccati equations (GAREs). To solve such equations, a novel scaling off-policy iteration (PI) algorithm is proposed. The key features of the proposed learning algorithm can be outlined in three main aspects: 1) during the learning process, the reliance on system dynamics is relaxed; 2) compared with the PI method, the requirement for initial admissible control policies is eliminated; and 3) a more rapid convergence speed is achieved than traditional value iteration. Finally, the effectiveness and advantages of the proposed method are validated through a simulation example and a series of comparative experiments.

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.

In this article, we focus on solving the problem of online multiplayer differential games (MDGs) of Markov jump systems (MJSs) using a reinforcement learning (RL) method. We consider MDGs of MJSs from the following two scenarios. In the first scenario, we propose a distributed minmax strategy, where each player can derive their optimal control policy from distributed game algebraic Riccati equations (DGAREs) without prior knowledge of the policies adopted by other players, distinguishing it from existing RL algorithms. We design a novel online distributed RL algorithm to approximate the solution of DGAREs without completely knowing system dynamics and initial admissible control policy. The second scenario involves applying Nash strategy to address MDGs of MJSs. Different from existing synchronous RL algorithm, we propose a novel online asynchronous RL algorithm that employs asynchronous iterative calculations for both policy evaluation and policy improvement, incorporating the latest information into the iterative process. The convergence of the designed RL algorithms is rigorously analyzed. Finally, two inverted pendulum system applications validate the effectiveness of the proposed methods.

This article proposes a new result on mismatched double fuzzy summation inequality. The mismatched double fuzzy summation inequality usually originates from the control system design with using the Takagi–Sugeno (TS) fuzzy model, in which the controller and the system are with mismatched membership functions. Compared with the existing method to deal with the mismatched double fuzzy summation inequality, the proposed one is less conservative and does not introduce additional decision variables. Three examples including the mismatched membership functions of the TS fuzzy control system in the discrete-time case, the continuous-time case and the interval type-2 fuzzy system under state feedback control mechanism are used to show that the new result is less conservative than the existing one.

Low-cost heading sensors and environmental interference lead to significant heading measurement error (HME) in Autonomous Surface Vehicles (ASVs), necessitating compensation to enhance path-following accuracy. To address this, we propose a compensated event-triggered line-of-sight (CET-LOS) guidance law. The core objective is to rapidly estimate and compensate for HME using an exponential estimation model. Specifically, the exponential model enables fast HME estimation, while an event-triggered mechanism, based on the convergence state of the cross-track error, ensures accurate error estimation. Additionally, the integral term from the integral LOS (ILOS) is utilized to refine the error estimation further. Experimental comparisons of CET-LOS with LOS, ILOS, and adaptive LOS (ALOS) demonstrate its effectiveness. For straight-line paths, the average cross-track error is reduced by 72.3%, 34.5%, and 62.3%, respectively. For complex paths, the reductions are 43.8%, 34.0%, and 29.2%, respectively. These results highlight the superiority of the proposed method.

The adaptive neural network (NN) control for the fixed-wing unmanned aerial vehicle (FUAV) under the unmodeled dynamics and the time-varying switching disturbance (TVSD) is investigated in this article. To better describe the TVSD induced by the change in the flight area of the FUAV, a switching augmented model (SAM) based on the known information about the TVSD is proposed first. The parameter adaptation technique is used to estimate the related TVSD. Thereafter, the time-varying disturbance that cannot be described by the SAM is estimated by the disturbance observer (DO). The radial basis function NN (RBFNN) is adopted to approximate the unknown unmodeled dynamics. The coupling terms derived from the co-design of DO and the parameter adaptation (PA) are separated by some inequality techniques. Then, the separated unknown terms are eliminated by designing the parameters of the controller and that of the adaptive law. The separated known terms are tackled by adding robust control terms to the controller. In addition, to improve the estimation performance for the TVSD and RBFNN, the auxiliary system in the DO form is designed. Sufficient stable conditions about the closed-loop switched system (CLSS) are obtained with and without the inequality about the switching times. Finally, an illustrative example is given to show the feasibility and advantage of the proposed control strategy by the attitude model of the FUAV.

This article investigates the robust data-driven containment control problem for heterogeneous multiagent systems where the system dynamics of both the leaders and the followers are unknown. During the data collection phase, the follower systems are disturbed by unmeasurable but bounded noises. A data-based linear matrix inequality condition is first constructed from the noisy data to determinate the feasible feedback gain for each follower. Then, the distributed observer which is independent of the system matrix of the leaders is designed to estimate the convex hull of the leaders. Moreover, the approximate solution to the linear matrix equation for heterogeneous system is solved with bounded approximate error where the noisy data of each follower and the normal data of arbitrary leader is utilized. Based on the proposed feedback gain and observer as well as approximate solution, the robust data-driven control protocol is provided to guarantee the uniform boundedness of containment error. Finally, a numerical example and a multivehicle model are given to verify the effectiveness of the designed containment control protocol.

This article investigates the global stabilization problem for a class of nonlinear systems via adaptive event-triggered output-feedback control. The distinguishing feature of the system mainly lies in the unknown control coefficient, the unknown growth rate (UGR) with polynomial-of-output growth (POG), the external disturbances and the unmeasurable states. In order to handle the above uncertainties, a dynamic high-gain and a high-gain-based observer are introduced. Aiming at reducing the computational costs and saving the communication resources, a dynamic event-triggering mechanism (ETM) is provided. An event-triggered controller based on the observer and the dynamic high-gain is designed by the technique of the backstepping and the method of the universal control. It can ensure global boundedness of all the closed-loop system states, and meanwhile, the Zeno behavior is avoided. A practical example is given to illustrate the correctness of the proposed method.

The work addresses the issue of observer-based control for networked nonlinear systems suffering from denial-of-service (DoS) attacks which are modeled in a novel persistent dwell time (PDT) switched form. The interval type-2 Takagi-Sugeno fuzzy model is adopted to accurately characterize the nonlinear feature in investigated systems and effectively capture uncertain parameters via membership functions. It should be noticed that a new framework comprising a special PDT switching strategy is proposed for distributed DoS attacks with energy limitation. Accordingly, the controlled system is converted into a complex and constrained PDT-switched system. Subsequently, based on weak multi-Lyapunov functions, membership-function-dependent sufficient criteria are offered for judging the globally uniformly exponential stability of the researched closed-loop system, from which the gains of the observer and the controller can be computed. Finally, an algorithm generating PDT switching sequences for distributed DoS attacks is proposed, and simulations are given to substantiate the usability of the developed approach.

This article investigates the problem of bipartite synchronization (BS) for coupled coopetitive memristive neural networks (MNNs) using a switching event-triggered gain-scheduled control strategy. First, a mathematical model of coupled MNNs exhibiting both cooperative and competitive interactions is formulated based on directed signed graph theory. To handle the antagonistic nature of these interactions, an orthogonal transformation is employed to develop a formally unified error system. Then, a switching event-triggered scheme (SETS) is designed, which leverages the coopetitive relationships among nodes to reduce communication costs. Meanwhile, a gain-scheduled controller, which incorporates the cooperative-competitive relationships is designed to achieve the BS. Specifically, the controller consists of both linear and nonlinear components: the linear component ensures system stability, while the nonlinear component compensates for residual terms arising from the heterogeneous structure of MNNs. Furthermore, the nonlinear control gains are scheduled via a function that depends on the error state, its derivative, and the sampled error, thereby reducing the conservatism of the synchronization conditions. A piecewise interval-dependent Lyapunov functional tailored to the characteristics of SETS is constructed. By employing inequality techniques, sufficient conditions for BS are derived in the form of linear matrix inequalities (LMIs), enabling the joint design of the linear control gains and the triggering matrix. To validate the proposed method, both a numerical example and a potential practical application are provided. In addition, two comparative studies are conducted to highlight the advantages of the proposed SETS and the interval-dependent Lyapunov functional, respectively.

In this paper, the issue of extended dissipative control for interval type-2 fuzzy Markov jump systems subject to actuator faults is investigated. It is different from the traditional T-S fuzzy method, the interval type-2 fuzzy set is introduced to model the system, and the nonlinearity with parameter uncertainty is captured using the interval type-2 Takagi-Sugeno fuzzy model. During the implementation of the control process, the possible actuator failures are fully considered when designing a controller, and a fault-tolerant control strategy is employed to enhance the elasticity of the designed controller. It is worth noting that, in order to better adapt to the characteristics of interval type-2 fuzzy system, the designed controller follows non-parallel distribution compensation rules, that is, the designed controller does not have the same premise structure and fuzzy rules as the system model, which further improves the flexibility of controller design. Some sufficient conditions ensuring the stability with the extended dissipative performance of the systems are established. In the meanwhile, the obtained results with less conservatism are ensured owing to the boundary information of membership functions considered in the stability analysis. Finally, two numerical examples are provided to indicate the effectiveness of the proposed method.

This paper investigates the security control of a class of discrete-time Markov jump systems (DMJS). Due to the vulnerability of open communication networks to cyber attacks, a hybrid attack model is established to describe the situation where the DMJS is simultaneously suffered from deception attacks (DAs) and denial-of-service (DoS) attacks. To cope with the intermittent characteristic of DoS attacks, adaptive memory-based event-triggered control (AMETC) with a switching-like adaptive law is proposed. The designed AMETC includes historical triggered data in its triggering condition, hence allows data transmission to adjust adaptively based on the long-term change of system state. In addition, when DoS attacks are launched by attackers, the designed switching-like adaptive law can help decrease the threshold to trigger more sampled data so as to stabilize the DMJS. These features contribute to improve the tolerance of the whole control system to DoS attacks and DAs. On the basis of the AMETC, a novel Lyapunov functional is designed, and sufficient conditions are derived to ensure the asymptotic stability of DMJSs. This functional plays a crucial role in ensuring the negative definiteness of the LMIs in the stability condition. Based on the stability condition, a design algorithm for security control gains and event-trigger matrices is given. Finally, simulation results validate the effectiveness and superiority of the proposed mechanism.Note to Practitioners—This paper was motivated by existing results on memory-based event-triggered control (METC) and hybrid cyber attacks. The aim of this paper is to design a novel AMETC with a switching-like adaptive law to cope with the impact of hybrid attacks on system performance. In practical applications, if the event-triggered mechanism only considers the transient information of the system, it may lead to high peak responses of the control systems, resulting in a decrease in system performance. Therefore, the AMETC proposed in this paper considers both the transient information and the long-term state change of the system. It can adjust the data transmission rate adaptively and save transmission resources effectively. Additionally, based on the designed AMETC, sufficient conditions are obtained to ensure the safe and stable operation of DMJSs. Our theoretical analysis and simulation results show the effectiveness and superiority of the designed mechanism.

In this paper, the asynchronous state estimation problem of two-time-scale Markov jump complex networks under analog fading channels is investigated, in which the mode of the designed state estimator is asynchronous to the system mode and the asynchronous probabilities are partially known. The two-time-scale phenomenon of complex networks is modeled by a singular perturbation parameter and the changes in connection mode between the complex networks are subject to a Markov chain. The output measurements are transmitted via analog fading channels and fading gains are used to represent the magnitude of the decline of the transmitted information. The purpose of this study is to design an asynchronous state estimator related to the asynchronous mode of the network topology such that the stochastic stability with an$H_{\infty}$performance index of the resultant error dynamics can be guaranteed. By designing the Lyapunov function which is associated with the singular perturbation parameter and the system mode, the stochastic stability condition of the estimation error system is derived. A new decoupling method to obtain the state estimator gains is proposed, which removes the limitation on the relaxation matrix of previous research results. Finally, the effectiveness of the methods are verified by a numerical simulation example and a capacitor-resistance circuit model.

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 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.

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.

In this paper, the cooperative output regulation problem is investigated for heterogeneous nonlinear multi-agent systems allowing frequent arrivals and departures of agents. Inspired a generally switched systems with multi modes and multi dimensions, the migration of agents is described by a switched communication network, where the number of agents and the graph of topology could be simultaneously changed at the switching instant. In this paper, this switching topology is modeled as a quasi-mode-dependent persistent dwell time switched system with size-varying modes for more generalization. Furthermore, the assumption of existing a directed spanning tree can be removed during the special time interval with a limit on the maximum dwell time, which makes the stability analysis for such switching directed topology more challenging. Hence, a quasi-time-dependent Lyapunov function is utilized to construct a novel distributed reference generator, which can approach the exosystem in a globally uniformly exponential manner. And based on the Takagi-Sugeno fuzzy set theory, a fuzzy cooperative controller is developed for each agent to track the estimating signal. Finally, a multi-pendulum system is adopted to illustrate the feasibility and applicability of the derived results.

This paper addresses the problem of adaptive constraint control for a class of uncertain nonlinear strict-feedback systems with full state constraints. Novel fuzzy logic-based adaptive adjustment techniques are adopted to help identify system uncertainties and unknown control coefficients. Then, a newly proposed nonlinear filter with adaptive parameter adjustment is employed to remove the complex calculation problem caused by repeated differentiation in the backstepping control design procedure. At the same time, the controller is constructed under the consideration that the state contains are not violated. Through the barrier Lyapunov theory analysis, the results that the system output signal tracks the desired trajectory and the tracking error converges to zero asymptotically are obtained. Finally, the numerical results of the comparative simulation verify the feasibility of the established control method.

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 article addresses a novel optimal fuzzy control scheme for a category of nonlinear Markov jump singularly perturbed systems with mixed zero-sum (MZS) game. First, a Takagi–Sugeno fuzzy structure is employed to describe the nonlinear plant. On this basis, two types of value functions for multiplayer MZS game are developed and a series of fuzzy controllers are designed. Subsequently, a hybrid iteration (HI) approximation learning framework is constructed for approaching the solutions of the fuzzy game coupled algebraic Riccati equations. The HI eliminates initial admissible control dependencies and has a desirable learning iteration rate, in contrast to the traditional policy iteration and value iteration. We present a novel model-based HI scheme and further develop it to a model-free version. The model-free HI scheme uses only online data collected along the system trajectories for controller design, and it eliminates the traditional model-dependent issues, which is more suitable for practical applications. Finally, the validity and flexibility of the proposed algorithm are verified by a simulation example.

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 paper focuses on the exponential stabilization issue of networked Lur’e systems (NLSs) suffering from DoS attacks. To conserve limited network resources and withstand aperiodic DoS attacks, a resilient memory-based event-trigger (RMET) mechanism is firstly designed. Then, based on an in-depth discussion on the relationship between the RMET scheme and DoS attacks, a comprehensive closed-loop system mathematical model is established. The model describes a clear switching characteristic of the whole control loop among waiting intervals, detection intervals, and DoS attacking intervals. On this basis, two multi-interval-dependent Lyapunov functionals (MIDLFs) are constructed. The primary advantage of the MIDLFs is that they can effectively exploit the information contained within the waiting intervals and DoS attacking intervals. Subsequently, an exponential stability criterion is derived by virtue of the continuity of the designed MIDLFs and the application of various inequality estimation techniques. Additionally, a co-design algorithm associated with the secure feedback gain and the event-trigger (ET) matrix is carried out. Finally, two numerical simulations are utilized to confirm the benefits of the RMET mechanism and MIDLFs in reducing the number of data packets as well as enhancing the tolerance ability of DoS attacks, respectively.

This paper investigates the secure synchronization control of Markovian jump neural networks (MJNNs) suffering from denial of service attacks (DoS attacks). The issue is presented for two reasons: 1) multiple sensors are generally used to measure the information of different state variables in practical networked control systems; 2) DoS attacks will disrupt the transmission of data packets in communication channels, thereby causing the system performance degradation. To deal with these problems, a multi-sensor-based switching event-triggered mechanism (SETM) is designed. More specifically, when the data packets are transmitted normally, the SETM will switch to an adaptive memory-based event-triggered mechanism, thereby saving the network resources. When the DoS attack occurs, the SETM will trigger immediately at the end of the DoS attack to improve the system performance. To facilitate the stability analysis, a merging time series is constructed by integrating the triggering instants of successful transmission and the ending instants of DoS attacks together. In light of the merging time series, a switched closed-loop system is established. Then, by utilizing the stability analysis idea of switched systems, a multiple Lyapunov functional is constructed, enabling the exploitation of multi-sampling. On this basis, a synchronization criterion is derived, accompanied by a co-design method for controller and the trigger matrices. Finally, the outcomes of the simulation confirm both the efficacy and the advantage of the suggested approach, especially when dealing with DoS attacks.

The collision avoidance in real-time is crucial for unmanned surface vehicles (USVs) in a complex environment. Traditional methods make it hard to ensure the balance of control decisions. To balance safety and practicality, a collision avoidance algorithm based on deep reinforcement learning (DRL) and a two-level incentive reward based on the principle of complementarity is proposed. To address the vital sparse reward problem of Deep Deterministic Policy Gradient (DDPG), the trajectory evaluation function of the dynamic window algorithm (DWA) is referred to construct the primary reward strategy, and a secondary incentive reward is constructed based on velocity obstacle (VO) to eliminate potential collision risks. To improve the efficiency of training, the electronic chart (EC) and Unity3D are used to build an immersive simulation platform. Based on it, simulations are made to verify the performance. In addition, field experiments are first conducted in various encounter scenarios to verify the effectiveness. The results show that it can take safe collision avoidance actions and get practical paths in various situations.

This article investigates the sampled-data-based secure synchronization control problem for chaotic Lur'e systems subject to power-constrained denial-of-service (DoS) attacks, which can block data packets' transmission in communication channels. To eliminate the adverse effects, a resilient sampled data control scheme consisting of a secure controller and communication protocol is designed by considering the attack signals and periodic sampling mechanism simultaneously. Then, a novel index, i.e., the maximum anti-attack ratio, is proposed to measure the secure level. On this basis, a multi-interval-dependent functional is established for the resulting closed-loop system model. The main feature of the developed functional lies in that it can fully use the information of resilient sampling intervals and DoS attacks. In combination with the convex combination method, discrete-time Lyapunov theory, and some inequality estimate techniques, two sufficient conditions are, respectively, derived to achieve sampled-data-based secure synchronization of drive-response systems against DoS attacks. Compared with the existing Lyapunov functionals, the advantages of the proposed multi-interval-dependent functional are analyzed in detail. Finally, a synchronization example and an application to secure communication are provided to display the effectiveness and validity of the obtained results.

This article is concerned with the event-triggered synchronization of Lur'e systems subject to actuator saturation. Aiming at reducing control costs, a switching-memory-based event-trigger (SMBET) scheme, which allows a switching between the sleeping interval and the memory-based event-trigger (MBET) interval, is first presented. In consideration of the characteristics of SMBET, a piecewise-defined but continuous looped-functional is newly constructed, under which the requirement of positive definiteness and symmetry on some Lyapunov matrices is dropped within the sleeping interval. Then, a hybrid Lyapunov method (HLM), which bridges the gap between the continuous-time Lyapunov theory (CTLT) and the discrete-time Lyapunov theory (DTLT), is used to make the local stability analysis of the closed-loop system. Meanwhile, using a combination of inequality estimation techniques and the generalized sector condition, two sufficient local synchronization criteria and a codesign algorithm for the controller gain and triggering matrix are developed. Furthermore, two optimization strategies are, respectively, put forward to enlarge the estimated domain of attraction (DoA) and the allowable upper bound of sleeping intervals on the premise of ensuring local synchronization. Finally, a three-neuron neural network and the classical Chua's circuit are used to carry out some comparison analyses and to display the advantages of the designed SMBET strategy and the constructed HLM, respectively. Also, an application to image encryption is provided to substantiate the feasibility of the obtained local synchronization results.

This article is concerned with the local stabilization of neural networks (NNs) under intermittent sampled-data control (ISC) subject to actuator saturation. The issue is presented for two reasons: 1) the control input and the network bandwidth are always limited in practical engineering applications and 2) the existing analysis methods cannot handle the effect of the saturation nonlinearity and the ISC simultaneously. To overcome these difficulties, a work-interval-dependent Lyapunov functional is developed for the resulting closed-loop system, which is piecewise-defined, time-dependent, and also continuous. The main advantage of the proposed functional is that the information over the work interval is utilized. Based on the developed Lyapunov functional, the constraints on the basin of attraction (BoA) and the Lyapunov matrices are dropped. Then, using the generalized sector condition and the Lyapunov stability theory, two sufficient criteria for local exponential stability of the closed-loop system are developed. Moreover, two optimization strategies are put forward with the aim of enlarging the BoA and minimizing the actuator cost. Finally, two numerical examples are provided to exemplify the feasibility and reliability of the derived theoretical results.

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 concerned with the integral-type event-triggered stabilization problem for Tahaki–Sugeno (T–S) fuzzy systems by employing the preassigned-interval looped function method. Herein, preassigned-interval looped function means that the positivity and symmetry on Lyapunov functions are removed in the inner preassigned intervals. The rest are saved. First, an integral-type trigger scheme is proposed to remember the evolution information of systems, which contributes to sampling the really necessary data packets. Then, a novel Lyapunov function is designed by using the integral of state information and preassigned intervals. In combination with some inequality techniques, continuous-time Lyapunov theory (CLT) and discrete-time Lyapunov theory (DLT), two sufficient conditions are developed to ensure the T–S fuzzy systems can be stabilized to the origin in the presence of an integral-type trigger scheme. Compared with some existing results, the advantages of the preassigned-interval looped function and trigger scheme are well analyzed. Finally, simulation results are carried out to verify the effectiveness of the control scheme.

This article investigates the passivity-based fuzzy control problem for discrete-time nonlinear semi-Markov jump singularly perturbed systems (SPSs) by the semi-Markov kernel (SMK) approach. Due to the fact that the statistical information of SMK is difficult to acquire completely, the generally partially available case, covering the completely available and completely unavailable cases, is considered to remove the ideal assumption that the system information is invariably available to controllers. Additionally, the mode-dependent fuzzy rules describing nonlinear behavior are constructed, that is, the membership functions are dependent on the system modes, thus promoting the previous results efficacious in a more accurate form. Subsequently, by fully utilizing available SMK information, some sufficient conditions based on the slow state feedback technique are derived to ensure that the semi-Markov jump fuzzy SPSs are stabilized and satisfy the given passive performance. Finally, the superiority and practicability of the presented theoretical results are explained by a numerical example and an inverted pendulum model.

This article is concerned with the exponential stabilization of switched neural networks (SNNs) with asynchronous switching. To save the limited bandwidth effectively, an adaptive periodic event-triggered mechanism (APETM) with a novel adaptive rule is excogitated, in which the threshold function is updated at each sampling instant to quickly respond to system changes and a tuning parameter is introduced to increase the adjustable range of the threshold function. A merging signal is constructed and then the closed-loop system is established to carry out stability analysis for asynchronous and synchronous switching cases within a unified framework. Then, based on the merging signal, a corresponding Lyapunov functional that includes a looped functional is constructed. This looped functional helps to reduce conservatism of the stability criterion. Finally, examples, including relevant discussions and comparisons, are shown to illustrate the efficacy of our developed results.

In this work, the non-fragile output feedback control problem is discussed for persistent dwell-time switched nonlinear systems . In order to make full use of the bandwidth in communication networks, the weighted try-once-discard protocol is adopted to connect only one sensor with communication networks at a instant via the maximum-error-first policy. It is worth noticing that via using the weighted try-once-discard protocol, the information received by the controller becomes less such that the gains of the controller may be more susceptible to the internal and external environment. Therefore, the non-fragile output-feedback fuzzy control scheme is employed for maintaining the performance of controllers under such a protocol. Then, based on suitable augmented multiple Lyapunov-like functions, sufficient conditions are derived to ensure the stochastically globally uniform exponential stability and a fixed H ∞ performance for the resulted closed-loop system. Furthermore, through solving those conditions, the design approach of the desired controller can be developed. The feasibility of the established approach is illustrated via a example and a truck-trailer model.

In this study, an innovative extended dissipative scalable control approach is proposed for monitoring islanded microgrids (ImGs), which has the advantage of flexibility for adding and removing distributed generation units (DGUs). Firstly, an arbitrary topology is developed in order to circumvent the connected constraints of subsystems in previous results. Secondly, the impact of subsystem indexing on total subsystem communications is investigated during the execution of scalable control algorithms, and an optimizable cost function is created to determine the cost related to subsystem indexing. Then, by using Lyapunov stability theory, several sufficient conditions under the subsystem-level are established to ensure the studied system are globally exponentially stable with the desired extended dissipative performance, which can guarantee that when adding DGUs in ImGs, only directly connected controller gains needed to be designed, and when removing DGUs in ImGs, no controller gains needed to be redesigned, that is, plug-and-play performance is achieved. Finally, the considered ImGs containing three DGUs and a numerical example are presented to demonstrate the effectiveness of the proposed control approach.

This paper addresses the bipartite leader-follower synchronization (BLFS) of cooperation-competition neural networks (CCNNs) via sampled-data (SD) control. First, the directed signed graph (SG) theory is applied to describe the cooperation and competition interactions, and thus, an appropriate mathematical model is constructed for such kind of multiple CCNNs. Based on the coordinate transformation technique, the main challenges encountered from the Laplacian matrix of the directed SG are circumvented. Then, a tractable error system can be established in the presence of SD control. Two interval-scheduled looped-functions are well-structured by relaxing the requirements of positive definiteness and continuity, respectively. In combination with discrete Lyapunov theory and some inequality techniques, some less conservative criteria are derived to guarantee the BLFS of CCNNs. Compared with the previous analysis methods, a smaller coupling strength or a larger sampling interval can be permitted on the basis of the developed results. Finally, two examples are presented to verify the effectiveness and advantages of the obtained results.

In this article, a novel integral reinforcement learning (RL)-based nonfragile output feedback tracking control algorithm is proposed for uncertain Markov jump nonlinear systems presented by the Takagi–Sugeno fuzzy model. The problem of nonfragile control is converted into solving the zero-sum games, where the control input and uncertain disturbance input can be regarded as two rival players. Based on the RL architecture, an offline parallel output feedback tracking learning algorithm is first designed to solve fuzzy stochastic coupled algebraic Riccati equations for Markov jump fuzzy systems. Furthermore, to overcome the requirement of a precise system information and transition probability, an online parallel integral RL-based algorithm is designed. Besides, the tracking object is achieved and the stochastically asymptotic stability, and expected$\mathcal {H}_{\infty }$performance for considered systems is ensured via the Lyapunov stability theory and stochastic analysis method. Furthermore, the effectiveness of the proposed control algorithm is verified by a robot arm system.

This article is concerned with the bipartite synchronization problem of coupled switching neural networks with cooperative–competitive interactions and reaction–diffusion terms. Different from the existing literature, the networked systems under investigation possess the relationship of cooperation and competition among nodes. Notably, the switching topology is described by a signed graph subject to the Markov jump process with the coexistence of positive and negative interaction weights. Specifically, a positive weight indicates an alliance relationship between two nodes and a negative one shows an adversary relationship. This article aims to design a bipartite synchronization controller for the aforementioned networks with the switching topology such that a prescribed$\mathcal {H}_{\infty }$bipartite synchronization is satisfied. Then, some sufficient criteria to ensure the stochastic stability of bipartite synchronization error systems are established in view of an appropriate Lyapunov function. Finally, two simulation examples are presented to verify the validity of the proposed bipartite synchronization control method.

In this article, a novel hybrid reinforcement$Q$-learning control method is proposed to solve the adaptive fuzzy$H_{\infty }$control problem of discrete-time nonlinear Markov jump systems based on the Takagi–Sugeno fuzzy model. First, the core problem of adaptive fuzzy$H_{\infty }$control is converted to solving fuzzy game coupled algebraic Riccati equation, which can hardly be solved by mathematical methods directly. To solve this problem, an offline parallel hybrid learning algorithm is first designed, where system dynamics should be known as a prior. Furthermore, an online parallel$Q$-learning hybrid learning algorithm is developed. The main characteristics of the proposed online hybrid learning algorithms are threefold: 1) system dynamics are avoided during the learning process; 2) compared with the policy iteration method, the restriction of the initial stable control policy is removed; and 3) compared with the value iteration method, a faster convergence rate can be obtained. Finally, we provide a tunnel diode circuit system model to validate the effectiveness of the present learning algorithm.

For a class of nonstrict-feedback stochastic nonlinear systems with the injection and deception attacks, this article explores the problem of adaptive neural network (NN) fixed-time control ground on the self-triggered mechanism in a pioneering way. After developing the self-triggered mechanism and the delay-error-dependence function, a neural adaptive delay-constrained fault-tolerant controller is proposed by employing the backstepping technique. The self-triggered mechanism does not require an additional observer to determine the time of the data transmission, which reduces the consumption of the system resources more efficiently. In addition, the whole Lyapunov function with the delay-error-dependence term is developed to solve the deferred output constraint problem. Under the proposed controller, it can be proven that all the signals within the closed-loop system are semiglobally uniformly bounded in probability, while the convergence time is independent of the initial state and the deferred output constraint control performance is achieved. The feasibility and the superiority of the proposed control strategy are shown by some simulations.

This article investigates the synchronization of communication-constrained complex dynamic networks subject to malicious attacks. An observer-based controller is designed by virtue of the bounded encode sequence derived from an improved coding-decoding communication protocol. Moreover, taking the security of data transmission into consideration, the denial-of-service attacks with the frequency and duration characterized by the average dwell-time constraint are introduced into data communication, and their influence on the coder string is analyzed explicitly. Thereafter, by imposing reasonable restrictions on the transmission protocol and the occurrence of attacks, the boundedness of coding intervals can be obtained. Since the precision of data is generally limited, it may lead to the situation that the signal to be encoded overflows the coding interval such that it results in the unavailability of the developed coding scheme. To cope with this problem, a dynamic variable is introduced to the design of the protocol. Subsequently, based on the Lyapunov stability theory, sufficient conditions for ensuring the input-to-state stability of the synchronization error systems under the communication-constrained condition and malicious attacks are presented. The validity of the developed method is finally verified by a simulation example of chaotic networks.

This article is concerned with the resilient sampled-data control for stabilization of Tahaki--Sugeno (T--S) fuzzy systems in the presence of denial-of-service (DoS) attacks. To describe the effects of DoS attacks, an appropriate mathematical model is established to characterize the complicated dynamical behaviors of DoS attacks and periodic sampling mechanism. On this basis, a resilient sampled-data control scheme including communication protocols and security controller gains, is proposed to account for the effects of DoS attacks. Meanwhile, a novel index is developed to measure the antiattack ability and security level. By utilizing the information of DoS attacks and resilient sampling intervals, a continuous interval-dependent Lyapunov function is constructed which does not increase at the resilient sampling instants. Then, according to the designed interval-dependent function, several inequalities estimation techniques, convex combination approach, and together with discrete-time Lyapunov theory, two sufficient criteria are derived to guarantee that the closed-loop T--S fuzzy systems are globally asymptotically stable with prespecified security levels subject to DoS attacks. The advantages of designed function are well analyzed in contrast with the previous discontinuous Lyapunov functionals. At last, two simulation examples are given to demonstrate the validity and effectiveness of the obtained results.

This article studies the asynchronous sliding mode control for nonlinear Markov jump systems (NMJSs), whose transition probabilities is piecewise-constant and subjected to the persistent dwell-time switching rule. By resorting to the interval type-2 Takagi–Sugeno (IT2 T-S) fuzzy set theory, the nonlinearity and uncertainty features of the investigated systems are modeled. Taking the uncertain membership functions (MFs) of IT2 T-S fuzzy systems into consideration, the nonparallel distributed compensation strategy is employed for designing the MFs of the controller such that they do not need to be obtained in time. Furthermore, in view of the difficulty of accurately acquiring the mode of NMJSs with complex transition probabilities, the hidden Markov model is employed to provide a detected mode for constructing the sliding mode law. Afterward, a novel fuzzy sliding surface is proposed, and an asynchronous IT2-fuzzy-model-based sliding mode control law is designed for forcing the closed-loop systems to move on the predefined sliding surface. Thereafter, a novel two-mode-dependent Lyapunov-like function is conceived for the studied systems, where modes of NMJSs and transition probabilities are permitted to change simultaneously. By applying an improved technique to handle the parameteric matrix inequalities, some less-conservative sufficient conditions in the form of linear matrix inequality are obtained for ensuring the stochastic exponentially asymptotical stability of the sliding mode with$H_\infty$performance. Finally, a mass-spring mechanical system is presented to substantiate the validity of the proposed main results.

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 paper focuses on the fuzzy control problem for a class of semi-Markov jump singularly perturbed nonlinear systems with actuator saturation. Nonlinearities in the underlying systems are tackled with the Takagi-Sugeno fuzzy model. As distinct from the previous achievements, the case that the semi- Markov kernel with partially known information is taken into account such that the underlying systems can be extended to a wider scope. Given that actuator saturation is ubiquitous in engineering systems, the convex hull method is embraced to address this circumstance. To reduce the conservativeness of the obtained results, on the basis of Lyapunov stability theory and LaSalle's invariance principle, some criteria related to the sojourn time are presented. Next, the mean-square exponential stability of the underlying system is obtained based on the derived results. Finally, further experiment to verify the feasibility of the proposed methodology is given by a tunnel diode circuit model.

In this article, the local stabilization problem is investigated for a class of memristive neural networks (MNNs) with communication bandwidth constraints and actuator saturation. To overcome these challenges, a discontinuous event-trigger (DET) scheme, consisting of the rest interval and work interval, is proposed to cut down the triggering times and save the limited communication resources. Then, a novel relaxed piecewise functional is constructed for closed-loop MNNs. The main advantage of the designed functional consists in that it is positive definite only in the work intervals and the sampling instants but not necessarily inside the rest intervals. With the aid of extended reciprocally convex combination lemma, generalized sector condition, and some inequality techniques, two local stabilization criteria are established on the basis of both the discrete- and continuous-time Lyapunov methods. The proposed analysis technique fully takes advantage of the looped-functional and the event-trigger mechanism. Moreover, two optimization schemes are, respectively, established to design the control gain and enlarge the estimates of the admissible initial conditions (AICs) and the upper bound of rest intervals. Finally, some comparison results are given to validate the superiority of the proposed method.

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 work explores the$H_{\infty }$synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the$H_{\infty }$performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation.

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.

In this article, the exponential synchronization of Markovian jump neural networks (MJNNs) with time-varying delays is investigated via stochastic sampling and looped-functional (LF) approach. For simplicity, it is assumed that there exist two sampling periods, which satisfies the Bernoulli distribution. To model the synchronization error system, two random variables that, respectively, describe the location of the input delays and the sampling periods are introduced. In order to reduce the conservativeness, a time-dependent looped-functional (TDLF) is designed, which takes full advantage of the available information of the sampling pattern. The Gronwall-Bellman inequalities and the discrete-time Lyapunov stability theory are utilized jointly to analyze the mean-square exponential stability of the error system. A less conservative exponential synchronization criterion is derived, based on which a mode-independent stochastic sampled-data controller (SSDC) is designed. Finally, the effectiveness of the proposed control strategy is demonstrated by a numerical example.

This article presents a decentralized learning control method for a class of partially unknown nonlinear systems with asymmetric control input constraints and mismatched interconnections via a novel dynamic event-triggering condition. By employing an integral reinforcement learning strategy, the system drift dynamics can be avoided in the learning process. Meanwhile, a critic neural network is designed to obtain the approximated value function and tuned by using the gradient descent approach. Furthermore, a novel dynamic event-triggering condition is designed to determine the occurrence of an event by introducing a dynamic variable. By using the Lyapunov theory, all signals in the closed-loop system are proved to be uniformly ultimately bounded. Finally, we present a nonlinear interconnected system and an interconnected power system to verify the effectiveness of the proposed method.

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.

In this article, the exponential stabilization (ES) of delayed switched linear systems with asynchronous switching is settled via discrete dynamic event-triggered (DDET) control. First, an observer-based DDET scheme with a prescribed constant upper bound is devised. The DDET scheme can avoid the Zeno behavior directly and reduce the communication burden immensely. And the prescribed constant upper bound for the trigger interval can prevent the system performance from deteriorating. Under the framework of the DDET scheme, the situation of no system switching or multiple system switching over a trigger interval is considered, and a unified closed-looped system (CLS) which incorporates the above two situations is established. Then, by constructing a series of multiple Lyapunov functionals, the exponential stability of the unified CLS is studied based on the average dwell-time (ADT) method. Moreover, a design procedure for the feedback gain is provided. Finally, a simulation example is given to illustrate the effectiveness and superiority of the DDET scheme and the obtained main results.

In this article, a memory-based event-trigger (MBET) scheme is designed to investigate the global synchronization problem of a class of Lur’e systems. There are two reasons for presenting this issue: 1) most of the existing event-trigger schemes do not take the historical state information into account and, therefore, may have some conservatism in reducing the number of triggering times and 2) the existing Lyapunov functionals cannot be directly utilized in the stability analysis of the resulting closed-loop system in this article. Motivated by the above-mentioned considerations, an MBET scheme, in which a time-memory term and an exponential decaying term are incorporated into the threshold function, is newly designed. It is beneficial to enlarging the intertrigger intervals and, thus, can further reduce the transmission of sampled data packets. Taking the features of MBET into consideration, a novel piecewise but continuous functional is constructed. On this basis, Lyapunov stability theory and some inequality estimation techniques are used to develop three linear matrix inequalities (LMIs)-based synchronization criteria and, meanwhile, a co-design for the control gain and the triggering matrix is carried out. Finally, some comparative analyses are provided to demonstrate the advantages of the MBET through an example of Chua’s circuit. Also, the application to information safety is given to verify the effectiveness of the synchronization results by using a 3-neuron neural network.

This work is devoted to an$H_{\infty }$sliding-mode control for nonlinear singularly perturbed system under a network control framework. Aiming at describing the nonlinearities with parameter uncertainties in the studied system, the idea of interval type-2 (IT2) fuzzy method is employed in the system modeling. A disturbance observer is developed to acquire the unknown disturbance, and the estimated value is implemented into the controller for neutralizing the influence of the disturbance. To further reduce the occupation of network resources, an event-triggered communication protocol with a dynamic threshold parameter is adopted, where the triggered condition can be adjusted adaptively as the evolution of the system states. Then, an appropriate fuzzy integral sliding motion is constructed based on the constructed system model and disturbance estimation. It is worth noting that the constructed fuzzy controller follows the idea of nonparallel distribution compensation, which improves the designed flexibility. In terms of fuzzy processing, by introducing the membership functions dependent method into the stability analysis, a series of relaxed criteria are established to ensure the global asymptotic stability for the closed-loop systems with$H_{\infty }$performance level. Finally, two classical examples of circuit model and inverted pendulum model are extended to IT2 fuzzy idea, showing the rationality of the proposed approach.

In this article, the problem of reachable set estimation (RSE) and the design of aperiodic sampled-data controller for Takagi–Sugeno fuzzy Markovian jump systems (T–S FMJSs) with unit-energy bounded disturbance (UEBD) and unit-peak bounded disturbance (UPBD) inputs are taken into consideration. First, sufficient conditions that all states of the T–S FMJSs are encompassed by ellipsoids under zero initial conditions are acquired via constructing a mode-dependent two-sided loop-based Lyapunov function and applying a linear matrix inequality approach. Second, the RSE is taken into account in the design of the state feedback aperiodic sampled-data controller with the aim that the resulting ellipsoid encompasses the reachable set of the closed-loop system. Finally, a nonlinear mass-spring model and a tunnel diode circuit model demonstrate the efficiency of the presented approach. In addition, this method is able to obtain a larger sampled-data period than other literature, thus saving bandwidth and reducing communication resources.

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.

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.

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 article focuses on the composite$\mathcal {H}_{\infty }$synchronization problem for jumping reaction–diffusion neural networks (NNs) with multiple kinds of disturbances. Due to the existence of disturbance effects, the performance of the aforementioned system would be degraded; therefore, improving the control performance of closed-loop NNs is the main goal of this article. Notably, for these disturbances, one of them can be described as a norm-bounded, and the other is generated by an exogenous model. In order to reject the above one kind of disturbance, a disturbance observer is developed. Furthermore, combining the disturbance observer approach and conventional state-feedback control scheme, a composite disturbance rejection controller is specifically designed to compensate for the influences of the disturbances. Then, some criteria are established based on the general Lyapunov stability theory, which can ensure that the synchronization error system is stochastically stable and satisfies a fixed$\mathcal {H}_{\infty } $performance level. A simulation example is finally presented to verify the availability of our developed method.

The issue of adaptive output-feedback stabilization is investigated for a category of stochastic nonstrict-feedback nonlinear systems subject to unmeasured state and unknown control directions. By combining the event-triggered mechanism and backstepping technology, an adaptive fuzzy output-feedback controller is devised. In order to make the controller design feasible, a linear state transformation is introduced into the initial system. At the same time, the Nussbaum function technology is used to overcome the difficulties caused by unknown control directions, and the state observer solves the problem of the unmeasured state. Based on the fuzzy-logic system and its structural characteristics, the issue of unknown nonlinear function with nonstrict-feedback structure in the system is tackled. The designed controller could not only guarantee all signals of closed-loop systems are bounded in probability but also save communication resources effectively. Finally, numerical simulation and ship dynamics example are given to confirm the effectiveness of the proposed method.

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 investigates the$\mathcal {H}_{\infty }$synchronization problem for fuzzy coupled neural networks subject to reaction–diffusion. An available control method, namely, the adaptive pinning control strategy, is employed. In view of such a method, one may accomplish control objectives by controlling a small number of nodes instead of all nodes, and in this regard, it is possible to reduce the control cost to some extent, and the method can adaptively adjust the coupling strength as well. Furthermore, a novel inequality is introduced, which can ensure that the developed results are less conservative compared with some existing ones of dealing with the reaction–diffusion terms. Then, through the utilization of fuzzy set theory together with Lyapunov stability theory, some sufficient conditions with the ability to ensure the$\mathcal {H}_{\infty }$performance level of the resulting synchronization error system are deduced. Finally, an illustrative example is presented to show the advantages and effectiveness of the proposed methods.

In this article, the fuzzy$\mathcal {H}_{\infty }$sliding mode control problem for continuous-time switched nonlinear systems is investigated. The switching signal conforms to the persistent dwell-time switching mechanism. The first objective of this article is to construct a switched integral sliding surface that not only accommodates the switched nonlinear model, but ensures that the sliding mode dynamics are globally uniformly exponentially stable and have an$\mathcal {H}_{\infty }$performance by the equivalent controller derived from the sliding surface. Another aim is to integrate the switched sliding mode control law to force the system trajectories to the sliding surface in a finite amount of time. Then, on the basis of the Lyapunov function technique, the specific form of the sliding mode control law gains is given first, and later the finite-time reachability of the sliding surface is ensured. Finally, the validity of the proposed switched sliding mode control method is validated by a numerical example.

In this article, the sliding mode control issue is investigated for a class of discrete-time Takagi–Sugeno fuzzy networked singularly perturbed systems via an observer-based technique. Moreover, to process the measurement output and schedule the transmission sequence for relieving the communication burden, a logarithmic quantizer and a weighted try-once-discard protocol are synthesized, which can further improve the network bandwidth utilization in networked control systems. Based on the fuzzy observer states, a novel fuzzy sliding surface is established by considering the singularly perturbed parameter properly, and we endeavor to synthesize a sliding mode control law such that the reachability of the prescribed sliding surface could be guaranteed. In addition, by virtue of the convex optimization theory and Lyapunov approach, sufficient conditions are developed to guarantee the asymptotic stability of the sliding mode dynamics as well as the error system with an expected$H_{\infty }$performance. Finally, a verification example is presented to illustrate the feasibility and effectivity of the proposed method.

This article discusses the interval type-2 (IT2) Takagi–Sugeno (T-S) fuzzy asynchronous controller design problem for nonlinear multiagent systems via a dynamic event-triggered scheme in the discrete-time context. To formulate the asynchronous phenomena between the system modes and the anticipant controller modes, the hidden Markov model is proposed. The primary attention is focused on the explicit design of the dynamic event-triggered strategy that can be dynamically adjusted in line with system information, which mitigates the communication burden efficiently. On this occasion, the information renewal of the controller is aperiodic. Furthermore, the nonlinear characteristics are effectually disposed through utilizing a unique IT2 T-S fuzzy model, which is with mismatched membership functions (MFs). As a result, the resulting closed-loop fuzzy multiagent systems are accompanied by mismatched MFs and asynchronous modes, whereafter, via solving the convex optimization problem, the desired controller gains are acquired. Eventually, the validity and practicability of the developed control scheme are illustrated by two examples.

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.

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.

In this paper, the state estimation problem for singularly perturbed switched complex dynamic networks (CDNs) is addressed, in which the persistent dwell-time (DT) switching mechanism is employed to depict the switchings among parameters of each node. In the aforementioned switching mechanism, the concept of stage consisting of the persistent portion and the DT portion is introduced. Based on the singular perturbation theory, a two-time-scaling variables containing fast and slow states are taken into account on the CDNs simultaneously, which make the constructed networks more realistic. Furthermore, the main aim is to design a mode-dependent estimator to track the state information that cannot be directly obtained and further ensures the global uniform exponential stability of the investigated systems with a generalized dissipativity property. Some sufficient criteria on the existence of the desired mode-dependent state estimator are established by utilizing a modified matrix decoupling method. Finally, the availability of the estimator design procedures is verified by a simulation example.

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.

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.

This article develops a generalized$\mathcal {H}$-representation, which can transforms stochastic systems into deterministic systems. With the help of this new approach, the necessary and sufficient conditions of$p$th moment asymptotic stability and stabilization of linear stochastic systems are addressed. Meanwhile, the generalized$\mathcal {H}$-representation method is also applied to investigate$p$th moment observability and$p$th moment complete observability for the addressed systems.

This article is concerned with the issue of dynamic event-based non-fragile dissipative state estimation for a type of stochastic complex networks (CNs) subject to a randomly varying coupling as well as fading measurements, where the variation of coupling is governed by a Markov chain. To characterize the measurement fading phenomenon for different nodes, a Rice fading model is considered with known statistics information of the coefficients. For the sake of further resource saving, a dynamic event-triggering strategy (ETS), which is proved to release less data packets than the static one, is implemented to govern the measurements transmission for each sensor to its corresponding estimator. The main objective of this article is to determine a dynamic event-based non-fragile estimator such that, for all possible parameter fluctuations in estimator gains, the estimation error system is stochastically stable with a strict (Υ1, Υ2, Υ3)-γ-dissipativity. Through intensive stochastic analysis, sufficient conditions are then derived in terms LMI to guarantee the existence of the desired state estimator. Finally, the effectiveness of the proposed results are verified by two practical examples of Chua's circuit and quadruple-tank process system (QTPS).

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.

In this paper, the finite-time sliding mode control issue is studied for a series of semi-Markov jump systems subject to actuator faults, where an asynchronous control method is adopted to overcome the non-synchronous phenomenon between the system mode and controller mode. Additionally, the event-triggered protocol, which determines whether the transmission of data should be performed according to the threshold condition, is introduced to alleviate the burden of data transmission in the communication channel. This paper aims to devise an asynchronous event-triggered sliding mode control law so as to guarantee the trajectories of the resulting closed-loop system can be forced onto the predefined sliding surface in a finite-time interval. Thence, by means of the mode-dependent Lyapunov functions and the finite-time theory, sufficient conditions are derived to assure that the closed-loop system is mean-square finite-time bounded in both reaching and sliding motion phases. Eventually, a numerical example and a tunnel diode circuit model are presented to illustrate the availability and practicability of the proposed approach.

This article focuses on the problem of fuzzy-model-based fault-tolerant control for nonlinear semi-Markov jump systems in the discrete-time context. The discrete-time semi-Markov process is employed to describe the mode jumping among several subsystems in the investigated systems. Moreover, the nonlinear characteristics are effectively tackled through utilizing the Takagi–Sugeno fuzzy model. Unlike some previous results, the information of the semi-Markov kernel (SMK) in this article is assumed to be partially available, which conforms better with the practical scenarios. Besides, considering the case that actuators may encounter some unexpected failures during system operation, the fault-tolerant mechanism is introduced in the process of controller design to enhance the fault tolerance of the studied systems. By means of SMK approach and Lyapunov stability theory, some elapsed-time-dependent criteria for guaranteeing the mean-square stability of the closed-loop system are established. According to these criteria, the design methodology of the reliable fuzzy state feedback controller is developed. Eventually, in order to verify the practicability and rationality of the developed control scheme, a single-link robot arm model is presented.

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 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.

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.

In this article, the issue of finite-time command filter-based adaptive fuzzy tracking control based on an event-triggered scheme for stochastic strict-feedback nonlinear systems is studied. By using a fuzzy logic system, finite-time command filter with compensation signals, an event-triggered adaptive controller is designed. The newly designed controller not only guarantees the property of finite-time convergence, but also reduces the communication burden from the controller to the actuator. Meanwhile, the problem of complexity explosion caused by the backstepping method is avoided by using command filter technology. The proposed controller can ensure that the output signal tracks the given reference signal under the bounded error. Finally, the simulation result proves the effectiveness of the proposed control method.

This article is concerned with the issue of l2- l∞state estimation for nonlinear coupled networks, where the variation of coupling mode is governed by a set of switching signals satisfying a persistent dwell-time property. To solve the problem of data collisions in a constrained communication network, the round-robin protocol, as an important scheduling strategy for orchestrating the transmission order of sensor nodes, is introduced. Redundant channels with signal quantization are used to improve the reliability of data transmission. The main purpose is to determine an estimator that can guarantee the exponential stability in mean square sense and an l2- l∞performance level of the estimation error system. Based on the Lyapunov method, sufficient conditions for the addressed problem are established. The desired estimator gains can be obtained by addressing a convex optimization case. The correctness and availability of the developed approach are finally explained via two illustrative examples.

This article deals with the extended dissipativity-based control issue for singularly perturbed systems (SPSs) with Markov jump parameters, in which the partial information issues of the Markov chain are fully considered. A comprehensive hidden Markov model (HMM) is established for the partial information issues on Markov chain, in which the transition probabilities of the hidden Markov state and the observation probabilities of the observed state are general, that is, the uncertainty and the unknown peculiarity of them may be encountered simultaneously. By using the HMM with general probabilities, a comprehensive criterion is derived to analyze the extended stochastic dissipativity of the hidden Markov jump SPSs with the different partial information issues on the Markov chain. Based on the derived criterion, an explicit expression to acquire the desired HMM-based controller is presented. An illustrative example and a vehicle active suspension system are, finally, show the validity of the established theoretical results.

This article study the asynchronous control problem for a class of discrete-time Markovian jumping Lur’e systems (MJLSs) over the finite-time interval. The partial accessibility of system modes with respect to the designed controller is described by a hidden Markov model (HMM). The asynchronous control law consists of two parts, i.e., the states and the nonlinearities involved in the dynamics of the controlled system. By selecting the appropriate Lyapunov functional and applying the modified sector condition, the finite-time stabilization conditions under the control constraints are derived. Finally, the effectiveness of the designed method is verified by an illustrative simulation.

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 deals with the issue of dissipative synchronization for Markov jump neural networks subject to time-varying interval delays by using the sampled-data control method. First, a new two-sided looped-functional is introduced to improve the informativeness of the formed Lyapunov-Krasovskii functional, which takes into an account the information of the entire sampling period. Compared with some traditional methods, the information between x(t) and x(tk+1) is also emphasized. On this basis, an improved inequality technique is considered valid to acquire the less conservative synchronization criterion. In the end, two numerical examples are displayed, and a comparative example powerfully demonstrates the availability and the superiority of the developed technique.

This article addresses the investigation of sliding-mode control (SMC) for slow-sampling singularly perturbed systems (SPSs) with Markov jump parameters. As a new attempt, the SMC strategy is considered in the study of discrete-time Markov jump SPSs. Subsequently, in order to design a sliding-mode controller to ensure the stability of the proposed system, a novel integral sliding surface is constructed, and an SMC law is synthesized to ensure the reachability of the sliding surface. Through the utilization of Lyapunov stability and SMC theory, sufficient conditions are derived to ensure the state trajectories of the system are driven to a predefined sliding surface and the closed-loop sliding mode dynamics are stochastically stable. Finally, the applicability of the proposed SMC strategy is verified by a numerical example and a practical electric circuit model.

This article addresses the quasi-synchronization problem of delayed memristive neural networks (MNNs) via hybrid event-triggered control. First, a hybrid event-triggering mechanism with a novel threshold function is devised. Therein, an exponential decay term and a non-negative constant term are additionally introduced. It can further extend the time span between two successively triggered events and therefore can reduce the amount of triggering times in comparison with some existing event-triggering mechanisms. Then, by constructing a time-dependent and piecewise Lyapunov functional, a less conservative criterion for quasi-synchronization of drive-response delayed MNNs is formulated in terms of linear matrix inequalities. In addition, an explicit expression of the error bound is provided and the design of the feedback gain is presented for a predetermined error bound. Finally, a numerical example is given to demonstrate the effectiveness of the theoretical analysis and the advantages of the proposed event-triggering scheme.

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 article investigates the reliable output feedback control problem for networked nonlinear semi-Markov jump systems, in which a control strategy with redundant channels is established to reduce the adverse effect caused by packet dropouts. The actuator faults are fully considered in the setup. On the basis of stochastic analysis theory and fuzzy-model-based method, some criteria are established to guarantee the σ -error mean-square stability for the considered systems. As a consequence, the reliable output feedback controller design method is proposed, which can be utilized to deal with the actuator failures problem effectively. Finally, two illustrative examples are employed to explain the availability of the presented design approach, where the single-link robot arm system model is contained.

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 article concentrates on the output feedback controller design problem for discrete-time nonlinear switched systems with actuator faults. The Takagi-Sugeno fuzzy model is adopted to approximate the nonlinearity of the plant with a set of local linear models. The persistent dwell-time (DT) switching law, which is more general than DT or average DT switching, is introduced to govern the switching among subsystems. In order to alleviate the effects of actuator failures on system stability and performance, a synthesized fault-tolerant output feedback controller ensuring various performance requirements is designed. Intensive attention is focused on establishing sufficient conditions, which can guarantee the exponential mean-square stability as well as the prescribed extended dissipativity property of the closed-loop system. By virtue of the Lyapunov stability theory and appropriate matrix transformation methods, the desired controller gains can be obtained by solving a convex optimization problem. The developed method is finally applied to address the control issue of a tunnel diode circuit system model to illustrate its efficiency and applicability.

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 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 is concerned with the event-based security control problems for a set of discrete-time stochastic systems suffered from randomly occurred attacks, especially the denial-of-service attacks and the deception attacks. An attack model with compensation is established to describe combinations of attacks to the networked control systems. An event-triggered mechanism is employed to debase the communication load by transmitting measurement signals when a definite triggering condition is satisfied. A definition of security probability is proposed to describe the transient state of controller systems. The aim of this paper is to design a dynamic output feedback controller, thereupon the closed-loop system achieves the described security in probability. Some novel sufficient conditions are proposed to guarantee the input-to-state stability of the system in probability, and the controller gains are designed by solving a set of matrix inequalities. The upper bound of the quadratic cost function is also derived. Finally, simulation examples are applied to illustrate the effectiveness of the proposed design scheme.

In this paper, the problem of generalized state estimation for an array of Markovian coupled networks under the round-Robin protocol (RRP) and redundant channels is investigated by using an extended dissipative property. The randomly varying coupling of the networks under consideration is governed by a Markov chain. With the aid of using the RRP, the transmission order of nodes is availably orchestrated. In this case, the probability of occurrence data collisions through a shared constrained network may be reduced. The redundant channels are also used in the signal transmission to deal with the frangibility of networks caused by a single channel in the networks. The network induced phenomena, that is, randomly occurring packet dropouts and randomly occurring quantization are fully considered. The main purpose of the research is to find a desired estimator design approach such that the extended (Ω1, Ω2, Ω3) - γ-stochastic dissipativity property of the estimation error system is guaranteed. In terms of the Lyapunov-Krasovskii methodology, the Kronecker product and an improved matrix decoupling approach, sufficient conditions for such an addressed problem are established by means of handling some convex optimization problems. Finally, the serviceability of the proposed method is explained by providing an illustrated example.

This paper concentrates on the fuzzy-model-based H∞ control for Markov jump nonlinear slow sampling singularly perturbed systems with partial information. The partial information problems including partial information on the transition probabilities of the Markov chain, on the Markov state, and on detection probabilities are taken into account simultaneously. A new hidden Markov model (HMM), in which some elements need not be known, is introduced to formulate the partial information problems. Some criteria on H∞ performance analysis and the existence of the desired HMM-based asynchronous fuzzy controller are derived. An optimized relaxation matrix is introduced to improve the decoupling method such that the obtained HMM-based asynchronous fuzzy controller is less conservative. Finally, two examples show the availability of the HMM-based asynchronous controller design procedures.

In this paper, the dissipative synchronization control problem for Markovian jump memristive neural networks (MNNs) is addressed with fully considering the time-varying delays and the fragility problem in the process of implementing the gain-scheduled controller. A Markov jump model is introduced to describe the stochastic changing among the connection of MNNs and it makes the networks under consideration suitable for some actual circumstances. By utilizing some improved integral inequalities and constructing a proper Lyapunov-Krasovskii functional, several delay-dependent synchronization criteria with less conservatism are established to ensure the dynamic error system is strictly stochastically dissipative. Based on these criteria, the procedure of designing the desired nonfragile gain-scheduled controller is established, which can well handle the fragility problem in the process of implementing the controller. Finally, an illustrated example is employed to explain that the developed method is efficient and available.

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.

This paper is concerned with the fuzzy-model-based nonfragile control problem for discrete-time nonlinear singularly perturbed systems with stochastic jumping parameters. The stochastic parameters are generated from the semi-Markov process. The memory property of the transition probabilities among subsystems is fully considered in the investigated systems. Consequently, the restriction that the transition probabilities are memoryless in widely used discrete-time Markov jump model can be removed. Based on the T-S fuzzy model approach and semi-Markov kernel concept, several criteria ensuring δ-error mean square stability of the underlying closed-loop system are established. With the help of those criteria, the designed procedures which could well deal with the fragility problem in the implementation of the proposed fuzzy-model-based controller are presented. A technique is developed to estimate the permissible maximum value of singularly perturbed parameter for discrete-time nonlinear semi-Markov jump singularly perturbed systems. Finally, the validity of the established theoretical results is illustrated by a numerical example and a modified tunnel diode circuit model.

This paper investigates the finite-time event-triggered 'I-1 control problem for Takagi-Sugeno Markov jump fuzzy systems. Because of the sampling behaviors and the effect of network environment, the premise variables considered in this paper are subject to asynchronous constraints. The aim of this paper is to synthesize a controller via an event-triggered communication scheme such that not only the resulting closed-loop system is finite-time bounded and satisfies a prescribed 'I-1 performance level, but also the communication burden is reduced. First, a sufficient condition is established for the finite-time bounded 'I-1 performance analysis of the closed-loop fuzzy system with fully considering the asynchronous premises. Then, based on the derived condition, the method of the desired controller design is presented. Two illustrative examples are finally presented to demonstrate the practicability and efficacy of the proposed method.

This paper is concerned with the nonfragileH∞control problem for discrete-time fast sampling Markovian jump singularly perturbed nonlinear systems described by the Takagi-Sugeno fuzzy model. By utilizing singular perturbation theory, a singular perturbation parameter (SPP) independent, i.e., ε-independent, condition is derived to make sure the underlying closed-loop system's stability and a mixedH∞and passive performance γ, simultaneously. The ill-conditioned case caused by SPP could be eliminated on the basis of such a condition. With the aid of the stochastic analysis approach, the desired controller gains can be obtained, where the nonfragile property is fully considered to improve the tolerance of controller. Furthermore, a technique is developed to estimate the upper bound of SPP ε in this paper by employing a useful inequality. The availability and practicability of the proposed design method are finally explained via a practical example of a tunnel diode circuit with a modified model and a numerical example.

This paper is devoted to the finite-time cluster synchronization issue of nonlinearly coupled complex networks which consist of discontinuous Lur'e systems. On the basis of the definition of Filippov regularization process and the measurable selection theorem, the discontinuously nonlinear function is mapped into a function-valued set, then a measurable function is accordingly selected from the Filippov set to ensure the existence of the solution for the discontinuous system. By designing the finite-time pinning controller, some sufficient conditions are obtained for cluster synchronization of the identical and nonidentical Lur'e networks, respectively. In addition, the settling time for achieving the cluster synchronization is estimated by applying the finite-time stability theory. And finally, a numerical example is presented to illustrate the validity of theoretical analysis.

This paper investigates the problem of stabilization of sampled-data neural-network-based systems with state quantization. Different with previous works, the communication limitation of state quantization is considered for the first time. More specifically, it is assumed that the sampled state measurements from sensor to the controller are quantized via a quantizer. To reduce conservativeness, a novel piecewise Lyapunov-Krasovskii functional (LKF) is constructed by introducing a line-integral type Lyapunov function and some useful terms that take full advantage of the available information about the actual sampling pattern. Based on the new LKF, much less conservative stabilization conditions are derived to obtain the maximal sampling period and the minimal guaranteed cost control performance. The desired quantized sampled-data three-layer fully connected feedforward neural-network-based controllers are designed by a linear matrix inequality approach. A search algorithm is given to find the optimal values of tuning parameters. The effectiveness and advantage of proposed method are demonstrated by the numerical simulation of an inverted pendulum.

This paper is concerned with the problem of extended dissipativity-based state estimation for discrete-time Markov jump neural networks (NNs), where the variation of the piecewise time-varying transition probabilities of Markov chain is subject to a set of switching signals satisfying an average dwell-time property. The communication links between the NNs and the estimator are assumed to be imperfect, where the phenomena of signal quantization and data packet dropouts occur simultaneously. The aim of this paper is to contribute with a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative, in the simultaneous presences of packet dropouts and signal quantization stemmed from unreliable communication links. Sufficient conditions for the solvability of such a problem are established. Based on the derived conditions, an explicit expression of the desired Markov switching estimator is presented. Finally, two illustrated examples are given to show the effectiveness of the proposed design method.

This study is concerned with the problem of the elimination of overflow oscillations (EOOs) for two‐dimensional (2D) digital filters with external interference. The main purpose is the presentation of a new unified criterion such that the underlying 2D digital filter is stable with a positive prescribed interference attenuation level. A performance index is proposed for the first time, which is referred to as generalised dissipativity property. By using this index and two harmonic slack matrices, a novel criterion is established, which can be used to solve ℋ ∞ EOOs, passive EOOs and l 2 – l ∞ EOOs for 2D digital filters with external interference in a unified framework, and reduce the conservatism of the existing results. The effectiveness of the criterion is demonstrated by a numerical example.

This paper considers the problem of stability analysis for uncertain stochastic neural networks with distributed delays and reaction-diffusions. Two sufficient conditions for the robust exponential stability in the mean square of the given network are developed by using a Lyapunov-Krasovskii functional, an integral inequality, and some analysis techniques. The conditions, which are expressed by linear matrix inequalities, can be easily checked. Two simulation examples are given to demonstrate the reduced conservatism of the proposed conditions.