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.

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

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

In this paper, a switched prescribed performance (SPP)-based tracking control problem is addressed for satellite attitude system with actuator faults, actuator saturation and external disturbance. To address the problems that actuator faults or saturation may cause the tracking error to violate the prescribed performance function (PPF), an SPP-based controller is designed. Unlike existing flexible or self-adjustable prescribed performance control (PPC) methods, which continuously adjust a single PPF, these methods may still suffer from singularity under actuator faults or saturation. The proposed control approach overcomes this limitation by employing a switching mechanism that switches to a more relaxed PPF when the tracking error is predicted to violate the original PPF. Furthermore, according to the output of the fuzzy logic system (FLS), a disturbance observer (DO) is designed. The stability of the closed-loop system is proven by applying the multiple Lyapunov function method. Finally, a numerical simulation is employed to demonstrate the effectiveness of the designed SPP-based control scheme.

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

This article investigates the sliding mode control (SMC) problem for a class of Markov jump systems (MJSs), in which the system states are sampled randomly and nonuniformly according to Markov chain. Besides, the transmission of sampled states through the shared network channel is inevitably subject to deception attacks obeying Markov model. In order to facilitate the subsequent design and analysis, the encountered three Markov chains are first mapped into one, meanwhile, a suitable mode detection scheme is put forward to simultaneously detect the partially inaccessible modes including the controlled system modes and attack modes. And then, a detected-mode-dependent sliding mode controller is designed to effectively cope with the stochastic features of sampling processes and attack occurrences. Furthermore, the reachability of the specified sliding surface and the mean-square exponential ultimate boundedness of the closed-loop system are analyzed and the corresponding conditions are derived. Finally, two simulation examples are provided to illustrate the designed control method.

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

This paper investigates neural network-based dynamic adaptive event-triggered security compensation feedback control for implicit power systems (IPSs) subjected to false data injection (FDI) attacks. By introducing a time-varying positive exponential term, a new dynamic adaptive event-triggered mechanism (DAETM), which can avert Zeno phenomenon and address the issue that the controller fails to cancel the internal impulses of singular systems when using period-sampling based event-triggered mechanisms, is proposed to achieve the goal of enhancing communication efficiency while relieving channel load. Via adopting radial basis function (RBF) neural network (NN) approximation method to estimate the unknown FDI attack signals, the event-based security compensation feedback (SCF) controller with adaptive corrective term is designed, which can compensate the FDI attack signals and minimize the effect of FDI attacks. Considering the designed DAETM as well as the adopted RBF NN approximation method, a novel Lyapunov-Krasovskii (L-K) functional is constructed, which captures the dynamic adaptive event-triggered information and facilitates to obtain the ideal adaptive law of NN weight matrix, thereby contributing to the realization of regularity, impulse-freeness and ultimate boundedness of IPSs and promoting the collaborative design of DAETM and SCF controller. The final simulation results for IPSs validate the effectiveness of the studied methodology.

This paper is concerned with the co-design problem of the adaptive neural sliding mode compensation controller and multidomain DoS-attack-aware scheduling protocol (MDSP) for the direct current microgrids (DC-MGs). It is considered that the process of transmitting the system data over wireless networks is vulnerable to intrusion by cyber-attacks, in which the aggressors might send denial-of-service (DoS) attacks to the output data and inject fake messages into the control signal. Due to the fact that not all data in DC-MGs can be acquired from direct measurements, an attack-parameter-dependent observer is introduced to estimate the unknown states under DoS attacks. Then, with the objective of enhancing the system responsiveness and bandwidth utilization, a novel MDSP is presented by introducing a multidomain structure, which enables to maintain the communication function of DC-MGs when some channels fail or are blocked. In the meantime, the neural network technique is employed to approximate unknown attack information based on the observer states, thus recovering normal data attacked by potential false data injection (FDI) attacks. Additionally, by introducing the partitioning strategy, the corresponding resilient finite-time boundedness for both the reaching phase and sliding motion phase is acquired, respectively. An adaptive sliding mode compensation controller is devised by introducing the additional compensation terms, which can improve the control accuracy and the adaptability to cyber-attacks, thus ensuring that the states of DC-MGs are rapidly entered and maintained on the sliding surface during a finite-time interval. Finally, the availability and validity of the presented approach is verified through a DC-MG with three constant power loads.

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

This paper investigates the static output feedback (SOF) control problem of T-S fuzzy systems based on higher-order Lyapunov functions (HO-LFs). Firstly, to alleviate the conservatism of controller design, an advanced high-order time-varying relaxation matrix (HO-TVRM) approach is proposed. The HO-TVRM approach can completely adapt to the homogeneous polynomial analysis framework and effectively utilize the potential characteristics of fuzzy control to broaden the feasible domain of the reference problem. Secondly, to deal with the non-convex constraints caused by SOF control, a more flexible switching sequence convex optimization (SSCO) algorithm is developed, which adopts a switching-type optimization variable structure and combines variable-weight-based switching mechanism to alternately linearize non-convex terms during the iteration process. As a result, a convex optimization problem that approximates the original problem is obtained. Finally, simulations are presented to testify the effectiveness of the relaxed SOF control strategy.

This article investigates the finite-time switching control issue based on switched observer for networked discrete implicit systems under dynamic event-triggered protocol (DETP) and denial-of-service (DoS) attacks. First, regarding the intermittent characteristic of DoS attacks, a switched observer adapted to such attacks is devised to tackle the unavailability of state information. In addition, to enhance the efficiency of network resource utilization, an improved DETP is introduced, which not only relies on the latest transmission data and the current sampling data, but also combines the changes in their average values to dynamically adjust the triggering threshold. Next, by adopting the singular value decomposition approach, the iterative technique and by constructing the Lyapunov-Krasovskii (L-K) functional correlated with the DoS attack signals, the criteria of singular finite-time boundedness for the networked controlled implicit systems are acquired. The proposed cooperative scheme for DETP and observer-based switching controller promotes both communication efficiency and the resilience of the implicit systems to DoS attacks. Finally, the validity of the mentioned collaborative design switching control method is confirmed by taking direct current motor model and tunnel diode circuit as simulation examples.

This research is concerned with$H_{\infty } $security feedback control for networked singular Markovian jump systems (NSMJSs) with actuator faults and denial of service (DoS) attacks under mode-dependent switching-like adaptive event-triggered scheme. To handle energy-limited denial of service attacks and further strengthen communication efficiency, a mode-dependent switching-like adaptive event-triggered scheme (MSAETS) adopting state-dependent adaptive threshold function is introduced. By exploiting singular value decomposition (SVD) technique and establishing a mode-related discontinuous Lyapunov-Krasovskii (L-K) functional, novel$H_{\infty } $stochastic admissibility criteria for NSMJSs with DoS attacks and actuator faults are acquired, and the relationship between event-triggered parameters and DoS attacks energy is developed through the clever use of matrices/vectors norms and iterative method. Collaborative design method of event-triggered scheme and security feedback controller is proposed, which can make sure the$H_{\infty } $stochastic admissibility of NSMJSs while alleviating the communication burden and improving communication efficiency. The validity of the suggested cooperative control strategy is demonstrated via using an oil catalytic cracking process. Note to Practitioners—In actual engineering, numerous dynamic systems are usually modeled as singular systems, such as oil catalytic cracking processes, power systems, etc. Noteworthy, the investigation of singular systems is considerably complicated and more challenging than normal ones since numerous factors have to be contemplated in parallel, including stability, regularity, non-impulsiveness or causality. Most existing control results are poorly suited to these types of practical systems. Meanwhile, practical systems inevitably endure sudden changes in system structure or/and parameters, actuators may fail completely or partially during operation, and open communication environment and limited bandwidth of communication networks pose a range of challenges including packet losses, network-induced transmission delays and malicious network attacks, all of which may lead to reducing system performance, compromising system stability and eventually result in catastrophic accidents. To address the above challenges, in this work, the mode-dependent switching-like adaptive event-triggered$H_{\infty } $security feedback control scheme is designed for NSMJSs with DoS attacks and actuator faults.

This study addresses the output feedback control synthesis problem for a quarter-vehicle active suspension system subject to input delay, using dynamic integral quadratic constraint. To account for the uncertain relationship between the real-time output of the controller and the delayed signal arriving at the actuator, a memory-driven control strategy is proposed. Furthermore, dynamic integral quadratic constraint multipliers are employed to reformulate the output feedback control synthesis into a set of linear matrix inequalities. A simplified memory-free dynamic output feedback controller is also introduced. Corresponding controller design algorithms are developed for both the memory-driven and memory-free schemes. Simulation results demonstrate the effectiveness of the proposed controllers in handling input delays, and comparative studies verify the superiority and feasibility of the proposed control approach.

This article investigates estimator-based attack-tolerant compensation control for discrete singular systems (DSSs) under false data injection (FDI) attacks and a hybrid internal dynamic variables (IDVs)-dependent event-triggered mechanism. With the aim of managing the transmission of measurement outputs in a more flexible manner to save the limited network bandwidth, a novel hybrid IDVs-dependent dynamic event-triggered mechanism is introduced in this paper. The triggering mechanism is characterized by having not only an additive IDV but also a multiplicative IDV related to the measurement output errors. In order to mitigate the adverse effects of FDI attacks on DSSs, an attack estimator and an attack-tolerant compensation controller are collaboratively designed, where the attack estimator can simultaneously estimate the system states and the cyber-attack signals. Using the singular value decomposition technique, regularity and causality of DSSs are proved, the ideal estimator gains and the attack-tolerant compensation controller gain are obtained simultaneously based on linear matrix inequalities, and new conditions for singularH∞ finite-time boundedness of DSSs are given. Lastly, a direct current motor-controlled inverted pendulum demonstrates the practicability and effectiveness of the estimator-based attack-tolerant compensation control method.

The problem of normalized proportional-derivative (P-D) and intermittent hybrid$H_{\infty}$control for delayed descriptor impulsive systems (DDISs) is studied in this work. The P-D and intermittent hybrid state feedback controller is designed, such that normalisation and$H_{\infty}$stabilisation of DDISs can be realised. A novel impulsive-inputs-dependent Lyapunov-Krasovskii functional based on a couple of auxiliary functions is constructed, which is continuous along the system state trajectories without imposing additional conditions on impulsive dynamics. Improved$H_{\infty}$stability criterion is proposed in the form of linear matrix inequalities by utilizing free weighting matrix technique, and the$H_{\infty}$stability criterion is described as a single condition based on a combination of discrete and continuous dynamics. Finally, the usefulness of the method set forth in this work is demonstrated by a two-loop circuit network.Note to Practitioners—This work is motivated by realizing normalized P-D and intermittent hybrid$H_{\infty}$control for delayed descriptor impulsive systems. In practical engineering, many dynamic systems have to be formulated as descriptor systems when modeling, such as circuit systems, vehicle suspension systems, and so on. It is worth pointing out that infinite dynamic modes will occur due to the algebraic constraints in descriptor systems, and can lead to internal impulsive behavior. At the same time, these systems are often accompanied by external impulsive perturbations, such as the sudden failure of a component in circuit systems, etc. The internal and external impulses can cause serious damage to the system performances and stability. Most of the existing control schemes are not well suited for such practical systems. In addition, due to the presence of time-varying delays, the delayed dynamic systems can easily collapse and cause significant damages. In this work, the P-D and intermittent hybrid$H_{\infty}$controller is designed so as to deal with the above challenges.

This article considers the problem of event-based$H_{\infty }$fuzzy control for implicit hybrid active vehicle suspension systems (IHAVSSs) embedding acceleration and Markovian jumping characteristics under random deception and denial-of-service (DoS) attacks. Aiming at conserving of communication resources, an event-triggered strategy is introduced in the communication link between sensor and controller. The accelerations of sprung, unsprung and tyre are taken into account, the active vehicle suspension systems are constructed as the implicit one. Admissibility conditions are derived under the framework of linear matrix inequalities by utilizing the piecewise Lyapunov-Krasovskii (L-K) functional. Via singular value decomposition (SVD) technique, the IHAVSSs are proved to be regular and impulse-free. By means of Takagi-Sugeno (T-S) fuzzy method and parallel-distributed compensation (PDC) technique, a mode-dependent (M-D) fuzzy feedback controller (FFC) is designed to ensure$H_{\infty }$stochastic admissibilization of IHAVSSs under random deception and DoS attacks. The validity of the results is demonstrated by using the quarter active vehicle suspension model produced by Quanser company. Note to Practitioners—The active vehicle suspension systems hold paramount significance in practical applications, effectively dampening vibrations from road surfaces and enhancing ride comfort. Concurrently, as networking advances, interconnectedness among various components within the active vehicle suspension system grows. However, the communication links are vulnerable to malicious attacks that may decrease the performance of the AVSSs, which inspires our current research. This paper delves into the$H_{\infty }$stochastic admissibilization of the AVSSs subjected to network malicious attacks, and makes full use of the dynamic variable of the system in the modeling process, that is, the accelerations of each components are selected as the system state variable. The method is applied to a platform simulation, and the stochastic admissibility of implicit hybrid active vehicle suspension systems under random deception and DoS attacks is realized.

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

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

This article investigates the sampled-data stabilization problem of Markov jump singular systems (MJSSs) with time-varying delay. First, using state decomposition method, the MJSSs are transformed into the form of algebraic and differential equations. Next, an extended Lyapunov–Krasovskii functional (LKF) with looped function is constructed by considering the new state vectors. Meanwhile, a relaxed integral inequality is proposed to estimate the integral terms generated during derivation. Based on the extended LKF and the relaxed integral inequality, several improved stochastically admissible criteria are established with a few of decision variables. Then, the design method of desired controllers with sampled-data is proposed for the addressed MJSSs. Finally, several numerical examples are provided to confirm the superiority of the proposed results.

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

This article focuses on the development of an observer-based finite-time Takagi–Sugeno (T–S) fuzzy sliding mode control strategy for unmanned marine vehicles (UMVs) under hybrid cyberattack scenarios. To relieve the communication burden for the industrial control network, a new dynamic memory-based event-triggered protocol (ETP) is properly put forward, which concerns the historically released data and two auxiliary dynamic variables. Due to the influence of aperiodic spoofing attacks and denial of service (DoS) attacks, the T–S fuzzy model of UMVs is transformed into a framework of stochastic switched T–S fuzzy systems. In the light of the T–S fuzzy observer, a memory-based T–S fuzzy sliding mode controller is constructed concerning the DoS attacks and spoofing attacks. Criteria of finite-time stable for the closed-loop stochastic switched T–S fuzzy systems are attained for the reaching stage and sliding mode stage. By means of the orthogonal decomposition method, a cooptimization method for the observer, controller, and weight matrix within the dynamic memory-based ETP framework is obtained. Eventually, a benchmark UMV is employed to validate the efficacy of the developed methodology.

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

In this article, a dual event-triggered (ET)-based fault-tolerant attitude flexible performance tracking control problem is studied for the satellite with input saturation, actuator fault and external disturbance. To make the prescribed performance function (PPF) be adjusted only when the attitude tracking error of the satellite violates the PPF, a predictive tracking error-based ET mechanism is designed. With the designed mechanism, the fault factor adaptive law and saturation difference are updated only when the tracking error violates the PPF. In addition, an ET command regulation scheme is designed, taking into account the performance of the system and the saving of communication resources. It is proved that the closed-loop system is stable by the Lyapunov method. Finally, the simulation results demonstrate the availability of the proposed tracking control scheme.

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

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

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

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

This article investigates the issue of distributed dual-terminal adaptive event-triggered admissible consensus tracking for linear leader-following descriptor multi-agent systems (DMASs) against asynchronous denial-of-service (DoS) attacks. Due to the various off/on moments and durations of consecutive asynchronous DoS attacks on different edges, the communication topologies of DMASs are considered to be randomly switched according to different types of DoS attacks, and the switched topologies are governed by Markov chains. To mitigate dispensable information transmission, a novel dual-terminal adaptive event-triggered protocol is put forward, which excludes the Zeno behavior in addition to flexibly adjusting the inter-event interval. Then, a local estimator based on state reconstruction is designed in a triggered way to exactly estimate agents’ states between two successive triggered moments. Additionally, by constructing a modified mode-dependent Lyapunov-Krasovskii functional and employing singular value decomposition technique, the frequencies and durations of DoS attacks are analyzed and the admissible consensus tracking conditions are acquired for DMASs when frequencies and durations of DoS attacks satisfy bounded conditions. Lastly, multiple transistor electrical circuit systems are employed to demonstrate the validity of the proposed strategy.

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

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

This paper is concerned with the attack detection problem of T–S fuzzy systems with unknown but bounded (UBB) noise subject to malicious attacks via the set-membership estimation and a switching multi-mode high-order free-weighting matrix (SMHFM) method. Initially, the SMHFM is developed to address the so-called conservatism problem caused by the one-order free-weighting matrix (OFM) method. To solve this problem, the homogeneous polynomial technique is employed to introduce groups of free-weighting matrices for different switching modes. As a result, the SMHFM is synchronized with the working modes and possesses a high-order feature. Additionally, a switching multi-mode mechanism is implemented to enable the SMHFM to exhibit multiple modes. Time-variant balanced matrices are introduced for different switching modes to adjust the relevant matrix terms. This adjustment allows for obtaining more relaxed conditions, leading to smaller constraint sets and higher accuracy in state estimation. Furthermore, a zonotope-based set-membership (ZS) attack detection algorithm is introduced for T-S fuzzy systems, which is capable of detecting various types of attacks. By utilizing the proposed SMHFM method for attack detection, the level of conservatism in state estimation can be reduced. Finally, two simulation examples are given and some comparisons are made to validate the effectiveness of the proposed methods.

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

This article investigates the relaxed resilient fuzzy stabilization problem for discrete-time Takagi–Sugeno (T–S) systems based on a new switching mechanism. First, a multimode switching fuzzy controller is constructed, which covers all the possible working modes of system dynamics. For the purpose of reducing conservatism, additional groups of gain matrices are introduced corresponding to different switching modes such that the switching controller switches along with the working modes. Then, the resilient fuzzy stabilization can be accomplished by the proposed controller when the condition of alarm threshold is satisfied. Second, regarding controller design, few groups of time-variant balanced matrices (TVBM) coping with different switching modes are introduced to update the linear matrix inequality (LMI) conditions for system stabilization. Thereby, the feasibility of LMI conditions can be enhanced by using the TVBMs to adjust the related matrix terms in the framework of the homogeneous polynomial technique. Therefore, larger feasible region and less conservatism can be attained by the developed switching mechanism than before even if the alarm threshold is assigned larger values. Finally, a benchmark example is provided to testify the effectiveness and superiority of the proposed method.

This paper proposes a novel approach to model predictive control (MPC) for Takagi-Sugeno (T-S) fuzzy systems by combining the homogeneous polynomial technique and a switching mechanism. Aiming at improving the overall system performance by dynamically switching between different controllers according to system dynamics changing, the proposed switching MPC (SMPC) scheme formulates a group of optimization problems to determine the optimal switching strategy and corresponding control actions. To be specific, the optimization problem is solved at each moment to generate a control sequence that optimizes the gain matrices to achieve the minimized performance index. Building upon the traditional MPC structure, the proposed SMPC method forms a new framework by reducing the conservatism of controller design in the solving process based on linear matrix inequality (LMI) conditions via introducing the homogeneous polynomial method. As a result, the proposed SMPC method enhances the feasibility of controller solving, improves control performance and expands the domain of attraction by utilizing the switching mechanism to handle the system dynamics more freely. Finally, the effectiveness and superiority of the proposed method are validated through different examples.

This paper investigates fault detection and robust security control of implicit Markovian jump (IMJ) systems affected by random false data injection (FDI) attacks and packet loss under memorized output-perceptive event-triggered protocol (MOETP). In order to harmonize control performance and communication efficiency, a mode-dependent MOETP is proposed by designing an output-dependent threshold function and introducing weight parameters. Fault detector and feedback controller incorporating random FDI attacks and packet loss information are cooperatively designed to detect the occurrence of system faults and achieve robust security control for IMJ systems. Considering the property that the derivative of “time-varying delay” function is 1 and the definition of error in MOETP, a novel mode-delay-related Lyapunov-Krasovskii functional is constructed so as to derive the strict linear matrix inequality criteria of$H_{\infty }$stochastic admissibility as well as the expected gains of fault detector and security controller based on free-weight matrix method and matrix transformation technique. The validity of the presented co-design methodology is confirmed by a tunnel diode circuit system.

In this article, the matter of aperiodic sampled-data$H_{\infty }$control of a quarter vehicle active suspension system with time-varying delay is investigated based on an uncertain discrete-time model. This article mainly solves three problems based on the theory of integral quadratic constraints. First, the system is discretized by resorting to lifting technique, the uncertainties of the discrete-time model are embodied by the operators related to sampled-data and time delay, in this process, a new norm upper bound estimation of operator is given. Second, based on the multipliers of the operator, the conditions for the improved$H_{\infty }$performance constraints are given. Third, with regard to controller design, the difficulties posed by the nonlinear coupling terms for solving the matrix inequalities are addressed by an iterative algorithm. Finally, the simulation platform and a practical example show that the proposed method is effect and can obtain more optimized results.

This article lays the spotlight on embedded-adaptive-observer-based$H_\infty$integrated fault estimation and memory fault-tolerant control for a nonlinear delayed implicit robotic arm. To tackle the nonlinearity of the system, a prototypical Takagi–Sugeno fuzzy method is deployed. An embedded adaptive proportional–integral observer program is proposed to implement$H_\infty$simultaneous estimation of the original system states, sensor faults, and actuator faults via the fault-embedded technique, embedding the sensor faults into the original system states and allowing them to be transformed into new substates. The development of a fresh fuzzy Lyapunov–Krasovskii functional with time delays, Markov jump modes, and fuzzy information creates a firm groundwork for the outcome of the fuzzy delayed implicit jump system meeting with stochastic admissibility and$H_\infty$performance metrics. Novel observer-based memory-aware fault-tolerant controller is engineered by taking the form of linear matrix inequalities to compensate the impact of time delays, perturbations, and faults. To illustrate the validity of the method put forward, a single-link robotic arm device is offered as an example finally.

In this article, an observer-based adaptive neural network (NN) event-triggered distributed consensus tracking problem is investigated for nonlinear multiagent systems with quantization. In the first place, the limited capacity of the communication channel between agents is considered. The event-trigger mechanism and dynamic uniform quantizers are set up to reduce information transmission. The next NN is utilized to handle the unknown nonlinear functions. Finally, in order to estimate the unmeasurable states, an NN-based state observer is designed for each agent by using a dynamic gain function. To settle the difficulty caused by the coupling effects of event-triggered conditions and the scaling function in dynamic uniform quantizers and observers, a distributed control protocol with estimated information of its neighbors is designed, which ensures distributed consensus tracking of the nonlinear multiagent systems without incurring the Zeno behavior. The effectiveness of the control protocol is illustrated by a simulation example.

This article is concerned with the static output feedback stabilization problem of T-S fuzzy systems by developing a novel switching sequence convex optimization (SSCO) algorithm. First, the time-varying relaxation matrix with time-varying properties is introduced, which combines the membership functions and the constructed switching mechanism to adjust the positive and negative terms of the inequality conditions, thereby improving the relaxation quality of stability conditions. To compute bilinear matrix inequalities (BMIs) with complex time-varying relaxation matrix structures, the developed SSCO algorithm employs a more flexible switching-type optimization variable and an inner approximation strategy capable of linearizing all concave sections. As a result, a sequence of linear matrix inequalities (LMIs) are obtained without introducing any additional constraint and with better approximation level than existing solving methods. The superiority of the proposed method includes two aspects: more relaxed solving conditions are gained by using the time-varying relaxation matrix technique to facilitate controller design; the proposed SSCO algorithm is of faster convergence rate, which means less computational time for practical implementation. Finally, the proposed relaxed switching control method is validated by simulation comparison.

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

The concept of reachable set and state space ellipsoid is used in this article to determine the optimal safe range of the truck-trailer driving by allowing the constrained variables to move freely within a given range. A method of returning the truck to the desired position is proposed by analysing the movement trajectory of the truck. Two results are certified. The first result considers the issue of reachable set estimation (RSE) for the multirate sampled-data (MRSD) system in the aperiodic sampled-data framework based on the model knowledge. By constructing loop-based Lyapunov functional (LBLF), we obtain the sufficient condition that all the state trajectories are confined to target ellipsoid. This article also provides a computational method for an MRSD controller considering RSE. The second result provides the data-driven control tactics for the unknown sampled-data system to consider the RSE problem for the aperiodic sampled-data system, using only the noisy data. In addition, this article extends the data-driven control scheme to the design of MRSD controllers and ensures the stability of the system in agreement with the measured data. Simulation results show that the MRSD controller under both the model-driven method and the data-driven method is valid and achieves better control effect compared to the single-rate sampled-data (SRSD).

This work addresses the issue of$H_{\infty }$adaptive sliding-mode control (SMC) for nonlinear delayed singular systems (NDSSs) under impulsive attacks (IAs). The NDSSs subjected to IAs are modeled as fuzzy delayed singular impulsive systems (FDSISs), and a neoteric fuzzy integral sliding surface function is constructed based on the piecewise auxiliary functions method, which is continuous on$(0,+\infty)$without any other additional conditions. Then, a suitable SMC law and an adaptive SMC law are designed, such that the state trajectories of FDSIS can be driven to the predetermined sliding surface in finite time. In addition, an improved impulse-time-dependent Lyapunov-Krasovskii functional containing characteristics of IAs and time delays is designed, and it is demonstrated to be continuous along the FDSIS trajectories. Improved conditions are put forward in the form of strict linear matrix inequalities via congruent transformation technology, which can guarantee that the singular sliding-mode dynamics under IAs fulfills$H_{\infty }$stochastic admissibilization. Finally, the practicability of the approach presented is attested and the effects of IAs on convergence time are testified by the networked truck-trailer system.

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

This article is concerned with developing a featured multi-instant Luenberger-like observer of discrete-time Takagi-Sugeno fuzzy systems with unmeasurable state variables, that is, not only to reduce the conservatism but also (at the same time) to alleviate the computational complexity over the recent approach reported in the literature. Contrary to previous approaches, an enhanced gain-scheduling mechanism is proposed for constructing much abundant working modes by online evaluating the updated variation information of normalized fuzzy weighting functions across two adjacent sampling instants and, thus, a different group of observer gain matrices with less conservatism is designed in order to employ the exclusive features for each working mode. Moreover, all the redundant terms containing both surplus and unknown system information are discriminated and removed in this study and, thus, the required computational complexity is reduced to a certain extent than the counterpart one. Finally, numerical examples are provided to illustrate the superiority of the developed approach.

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

The problem of relaxed state estimation of discrete-time Takagi-Sugeno fuzzy systems is studied by constructing a novel multi-instant gain-scheduling fuzzy observer. First, a multi-instant gain-scheduling mechanism with a single adjustable parameter is given for the first time in order to produce more reasonable switch modes over previous results reported in recent literature. Second, for every switch mode, a batch of specified observer gain matrices is determined by developing an efficient balanced matrix approach so that the updated values of adjacent normalized fuzzy weighting functions can be flexibly exploited. Since the implied information of each specific switch mode is capable of being absorbed and utilized more thoroughly by the aid of the refined higher-order balanced matrices, the conservatism can be prominently reduced at the price of consuming extra computational burden within the allowable range. Finally, two benchmark examples are provided to test and verify the progressiveness of our proposed approach.

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 researches$H_{\infty }$asynchronous admissibilization for nonlinear singular delayed hybrid hydraulic turbine governing systems with impulsive perturbations. By exploiting Takagi–Sugeno fuzzy technique, the fuzzy singular delayed hybrid hydraulic turbine governing system with impulsive perturbations is built, which cannot only accurately and concisely describe the dynamic behaviors of the pipeline, but also resist damages for the hydraulic turbine system caused by sudden reasons. Then, the asynchronous fuzzy state-feedback controller (FSFC) is designed by using parallel distributed compensation technique, and the asynchronous mechanism between the modes of FSFC and the modes of controlled plant is depicted by a hidden Markovian model. By constructing an improved timer-dependent Lyapunov–Krasovskii functional, the new criteria about$H_{\infty }$admissibilization for fuzzy singular delayed hybrid hydraulic turbine governing systems with impulsive perturbations can be acquired. The gains of asynchronous FSFC are expressed by solving linear matrix inequalities. Impulsive operators are successfully generalized and extended from$\mathbb{R}$to$\mathbb{R}^{n\times n}$, which makes the approach presented in this article more universal. Finally, the simulation results prove the correctness and availability of the method provided in this article.

The constrained Takagi–Sugeno fuzzy systems with local nonlinear models (N-TS fuzzy systems) are much more practical than the conventional T–S fuzzy models, but their applications are seriously affected by either the great conservatism or the heavy computational burden. In this short article, an enhanced local stabilization of constrained N-TS fuzzy systems is investigated with the purpose of not only reducing conservatism but also relieving computational burden. To do this, a new class of threshold functions are established in order to achieve real-time online assessment of the variation of the normalized fuzzy weighting functions (NFWFs) at different sampling instants, i.e., the one-step-past one and the current one. Thus, a key alternative is proposed for eliminating all the involved terms containing the one-step-past NFWFs while their main information is retained. Since our obtained design criteria are merely dependent on the current NFWFs, the conservatism can be reduced at the expense of lighter computational burden. The benefits of our proposed results are validated by implementing several objective comparisons with those recent results.

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

This article emphasizes the$H_{\infty }$control problem with convergence rate constraint for time-varying delay switched systems under the average dwell time switching signal. First, a novel criterion of asymptotic stability with$H_{\infty }$performance is advanced by introducing the method of average dwell time and the definition of asymptotic interval stability, which is more exact than the general stability criterion in estimating the convergence of the systems. Next, the technique of structuring$H_{\infty }$controller is derived from the stability analysis described above, which can constrain the rate of the system to equilibrium, and make it content the preset$H_{\infty }$performance. Finally, a numerical calculation and a water pollution control issue demonstrate the functionality of the technique.

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, 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.

In this article, relaxed local stabilization criteria of constrained N-TS fuzzy systems are provided by developing a weighted ranking protocol. Different from the recent criteria reported in the literature, a set of operating modes is produced by employing the weighted ranking information of the available normalized fuzzy weighting functions. With regard to each enabled operating mode, its proprietary boundary information constraint is fully considered by means of introducing a class of cascade-type free variables, and thus the conservatism can be reduced to a large extent than before. More importantly, the applied variable weights for adjusting the range of different operating modes play an important role in further enlarging the estimated attraction regions. Finally, the superiority of the proposed approach is tested and verified by giving several comparisons on two key indicators for the same benchmark example.

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

The problem of robust nonlinear fault estimation for discrete-time Takagi-Sugeno fuzzy systems is investigated by proposing a depth partitioning approach. Compared with those existing results, the working space spanned by the updated normalized fuzzy weighting functions can be partitioned into any number of non-overlapping subspaces in a systematic way and thus a new gain-scheduling fault estimation observer is developed with proprietary gain matrices concerning the current operating mode. More importantly, the conservativeness will become less and less if the prescribed number of non-overlapping subspaces varies from small to large. In other words, it provides a chance to achieve much better performance of fault estimation while a smaller degree of the homogenous matrix polynomial can be chosen, which is very important for practical applications of theoretical results. Finally, the given depth partitioning approach is applied to deal with the fault estimation of the tunnel diode circuit and its advantages are validated via simulation comparisons.

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 studies the finite-time tracking control problem for the single-link flexible-joint robot system with actuator failures and proposes an adaptive fuzzy fault-tolerant control strategy. More precisely, the issue of "explosion of complexity" is successfully solved by incorporating the command filtering technology and the backstepping method. The unknown nonlinearities are identified with the help of the fuzzy logic system. An event-triggered mechanism with the relative threshold strategy is exploited to save communication resources. Furthermore, the proposed control design can guarantee that the tracking error converges to a small neighborhood of origin within a finite time by taking full advantage of the finite-time stability theory. Finally, the simulation example is presented to further verify the validity of the proposed control method.

This article focuses on the design of a novel adaptive fuzzy event-triggered tracking control approach for a category of high-order uncertain nonlinear systems with prescribed performance requirements, in which a high-order tan-type barrier Lyapunov function (BLF) is employed to handle and analyze the output tracking error, fuzzy systems are adopted to identify the totally unknown nonlinear functions, and only one gain function rather than parameter estimation functions is designed to cancel out all unknowns appearing in fuzzy systems. As a result, complicated calculations are avoided and a structured simple control is achieved. The proposed controller not only ensures that the tracking error is always within a predefined region but also reduces the communication burden from the controller to the actuator. Finally, comparison simulations are presented to verify the effectiveness of the proposed control schemes.

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$\mathcal {H}_{\infty }$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 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 study is concerned with proposing much more effective Luenberger-like fuzzy observers than relevant multiinstant observers reported in recent literature, in other words, less conservative results can be given while less computing resource is consumed as an important premise. For the first time, the variation bound of two adjacent time-variant Lyapunov matrices encountered in the synthesis of fuzzy observer is characterized by a contraction factor, and thus the key replacement of the lagged term is developed in order to construct an effective synchronization framework of fuzzy observer synthesis. Since there only exist the current-time normalized fuzzy weighting functions in the obtained synchronization framework, the conservatism caused by the mismatch of different sampling instants can be avoided. Based on this, a lightweight gain-scheduling law is designed in which a large number of redundancy gains in recent multiinstant observers can be condensed in order to save computing resource to the greatest extent. Finally, two benchmark examples are borrowed for validating the superiority of our proposed results.

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

This article addresses an event-triggered adaptive fuzzy tracking control problem for nonlinear systems with unknown control directions. To achieve the practical tracking, fuzzy logic systems are employed to approximate unknown nonlinear function. Nussbaum functions are adopted to address the problem of the unknown control directions. The newly designed controller not only guarantees the tracking error that converges to an arbitrary small neighborhood near the zero but also reduces the communication burden from the controller to the actuator. Moreover, the feasibility of the proposed event-triggered mechanism is verified by excluding Zeno behavior. Finally, the simulation result proves the effectiveness of the designed control scheme.

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.

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 reports our study on asynchronous H∞filtering for fuzzy singular Markovian switching systems with retarded time-varying delays via the Takagi-Sugeno fuzzy control technique. The devised parallel distributed compensation fuzzy filter modes are described by a hidden Markovian model, which runs asynchronously with that of the original fuzzy singular Markovian switching delayed system. The fuzzy asynchronous filtering dealt with in this article contains synchronous and mode-independent filtering as special cases. Novel admissibility and filtering conditions are derived in terms of linear matrix inequalities so as to ensure the stochastic admissibility and the H∞performance level. Simulation examples including a singlelink robot arm are employed to demonstrate the correctness and effectiveness of the proposed fuzzy asynchronous filtering technique.

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.

A novel adaptive fuzzy control scheme is designed in this article for a class of stochastic nonstrict feedback nonlinear systems with output constraint and unknown control coefficients. A reduced adaptive fuzzy system is proposed to approximate the unknown function which contains all state variables of the whole system and ensures that the backstepping design method works normally for nonstrict feedback nonlinear systems. With the use of this reduced adaptive fuzzy control method and a combination of Barrier Lyapunov Function control design and Nussbaum gain technique, a novel adaptive fuzzy controller is proposed to guarantee that the output tracking error always meets the given constraint requirement in a sense of probability and the resulting closed-loop states are bounded in probability. Finally, an example is presented to confirm the effectiveness of the designed method.

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 asynchronous feedback control for fuzzy degenerate jump systems with mode-dependent time-varying delays via Takagi–Sugeno fuzzy control technique under observer-based event-driven characteristic. The improved observer and event trigger transmit not only state estimate signals but also the information of Markovian jump modes to the controller under the networked nonperiodic sampling scheme. Applying parallel distributed compensation technique, the asynchronous fuzzy feedback controller is devised, and the asynchronous fuzzy controller modes are depicted by a hidden Markovian model, where the modes of fuzzy controller run asynchronously with that of the original degenerate fuzzy jump systems. Exponentially decreasing function is employed to devise the triggering threshold, which can ensure not only the admissibility of the delayed fuzzy degenerate jump systems but also the effectiveness of event-driven strategy. Original stochastic admissibility and asynchronous feedback control conditions are characterized by linear matrix inequalities. A numerical example and a single-link robot arm model are applied to illustrate the correctness and validity of the proposed observer-based event-driven asynchronous fuzzy feedback control technique.

In this article, the stability issues are addressed for switched linear systems (SLSs) with mode-dependent average dwell time (MDADT). By dividing the dwell time into several segments, and constructing a reverse timer which starts timing at the end of each segment, we propose a new reverse-timer-dependent multiple discontinuous Lyapunov function (RTDMDLF), which is more general than the multiple Lyapunov function (MLF) and the multiple discontinuous Lyapunov function (MDLF). With the help of the RTDMDLF approach, several convex and nonconvex stability conditions are derived for SLSs with both stable and unstable subsystems, and the relation of these conditions and existing ones is revealed. Moreover, the stability conditions for SLSs with all stable subsystems are also given. All the results are presented in terms of infinite-dimensional linear matrix inequalities (LMIs), which can be relaxed into computable conditions by using a discretized approach. It is shown that the tighter bound of MDADT can be achieved by the RTDMDLF approach compared with those of the literature. Finally, the advantages of the results are illustrated within three numerical examples.

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.

The issue of adaptive fuzzy prescribed performance tracking control is considered in this article for strict-feedback stochastic uncertain nonlinear systems. A novel adaptive tracking control design approach, in which the fuzzy systems are employed to approximate the totally unknown nonlinear terms, is proposed by incorporating the technology of prescribed performance control with the method of command filtered backstepping design. The proposed adaptive state feedback controller can ensure that the output tracking error converges to a predefined arbitrarily small residual set in probability and all the signals of the closed-loop system can be bounded in probability, meanwhile, the problem of “explosion of complexity” is solved. The effectiveness of the presented method is verified by two simulation examples.

To deal with a class of nonlinear systems with unknown control directions, a command filter-based adaptive tracking controller is designed in this paper. In the design process, fuzzy logic system is required to handle nonlinear functions, command filter is employed to settle the explosion of complexity problem and Nussbaum function is introduced to compensate the influence of unknown directions problem. Finally, the proposed control approach guarantees that error signals converge into bounded compact sets around the origin and all closed-loop signals are bounded. The effectiveness of the presented scheme is illustrated by a simulation example.

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.

This article considers admissibility analysis and stabilization for implicit Markovian jump systems (IMJSs) with retarded discrete-distributed delays. Admissibility analysis is investigated for the unforced delay IMJSs by virtue of reciprocally convex integral inequality technique and Barbalat’s lemma. State feedback controller is designed via the matrix transformation technique to realize the stabilization of the delayed closed-loop IMJSs. By selecting comprehensive L-K functional with modes and delays information, admissibilization conditions are presented in terms of LMIs. Two illustrative examples including an inverted pendulum controlled by a direct current motor (DCMCIP) system are utilized to certify the effectiveness and practicality of the admissibilization technique.

This paper focuses on the practical output tracking control for a category of high-order uncertain nonlinear systems with full-state constraints. A high-order tan-type barrier Lyapunov function (BLF) is constructed to handle the full-state constraints of the control systems. By the BLF and combining a backstepping design technique, an adding a power integrator, and a fuzzy control, the proposed approach can control high-order uncertain nonlinear system with full-state constraints. A novel controller is designed to ensure that the tracking errors approach to an arbitrarily small neighborhood of zero, and the constraints on system states are not violated. The numerical example demonstrates effectiveness of the proposed control method.

This paper reports our study on adaptive tracking control for a mobile-wheeled inverted pendulum with periodic disturbances and parametric uncertainties. With an appropriate reduced dynamic model, incorporating repetitive learning strategies with dynamic decoupling and related adaptive control techniques, a novel controller is successfully constructed to ensure that the output tracking errors of the system will stay within a small neighborhood around zero and all of the other signals are semiglobal uniform bounded. Meanwhile, only one parameter estimation is used for adaptive controller design, which overcomes the problem of over-parametrization. Furthermore, a required condition of period identifier mechanisms is proposed. Finally, detailed simulation results are presented to demonstrate the effectiveness of the proposed control schemes.

In this paper, for the case of the asynchronous switching caused by that subsystem's switching occuring during a sampling interval, the domain of attraction estimation problem is investigated for aperiodic sampled-data switched delayed neural networks (ASDSDNNs) subject to actuator saturation. A parameters-dependent time-scheduled Lyapunov functional consisting of a novel looped-functional is constructed using segmentation technology and linear interpolation. By employing this novel functional and using an average dwell time (ADT) approach, exponential stability criteria are proposed for polytopic uncertain ASDSDNNs subject to actuator saturation. And a relationship between ADT and sampling period is revealed for ASDSDNNs. As a corollary, exponential stability criteria are proposed for nominal ASDSDNNs subject to actuator saturation. Furthermore, by describing the domain of attraction as a time-varying ellipsoid determined by the time-scheduled Lyapunov matrix, the proposed theoretical conditions are transformed into a linear matrix inequality (LMI)-based multi-objective optimization problem. The dynamic estimates of the domain of attraction for ASDSDNNs are solved. Numerical simulation examples are provided to illustrate the effectiveness of the proposed method.

A fractional-order integral fuzzy sliding mode control scheme is proposed for a class of uncertain fractional order nonlinear systems subject to uncertainties and external disturbances. First, in each step, a neuro-fuzzy network system is developed to approximate the uncertain nonlinear function existing in fractional-subsystem and a fractional sliding mode surface is presented. Second, based on the fractional Lyapunov stability theory and the finite-time stability theory, a fractional adaptive backstepping neuro-fuzzy sliding mode controller is designed to drive the state trajectories of fractional-order systems to the prescribed sliding mode surface. Meanwhile, the finite-time stability of the fractional-order closed-loop system is proved. At last, three numerical examples are given to illustrate the effectiveness of the proposed control method.

This paper is concerned with quasi-synchronization of delayed memristive neural networks (MNNs) with switching jumps mismatches via aperiodically intermittent control. The issue is presented for three reasons: 1) the existing controllers for synchronization may be too complicated and not economical; 2) under the influence of switching jumps mismatches, synchronization of MNNs may fail to achieve; and 3) matrix measure method is less conservative but cannot be applied directly to synchronization of MNNs. To overcome these difficulties, the concept of asynchronously switching time interval is proposed to describe the phenomenon when the drive-response MNNs switch their connection weights asynchronously. Then, aperiodically intermittent control is designed and quasi-synchronization analysis is carried out based on a combined method that compromises the merits of interval matrix method and matrix measure method. A quasi-synchronization criterion, expressed in terms of the mixture of p-norm and matrix measure of the memristive connection weights, is established. Meanwhile, the fundamental reason for the failure of complete synchronization is revealed. Moreover, an explicit expression of the error level is obtained and the design of the controller under a predetermined error level is presented. The obtained results in this paper reduce the conservativeness and provide a novel insight into the research of synchronization of MNNs.

This paper reports our study on adaptive fuzzy tracking control for flexible-joint robots with full state constraints. In the control design, fuzzy systems are adopted to identify the totally unknown nonlinear functions and can properly avoid burdensome computations. The tan-type barrier Lyapunov functions are used to deal with state constraints so that even without state constraints, the controller is still valid. By combining the method of backstepping design with adaptive fuzzy control approaches, a novel simpler controller is successfully constructed to ensure that the output tracking errors converge to a sufficiently small neighborhood of the origin, while the constraints on the system states will not be violated during operation. Finally, comparison simulations are presented to demonstrate the effectiveness of the proposed control schemes.

In this paper, an adaptive fuzzy controller is constructed to address the finite-time tracking control problem for a class of strict-feedback nonlinear systems, where the full state constraints are strictly required in the systems. Backstepping design with a tan-type barrier Lyapunov function is proposed. Meanwhile, fuzzy logic systems are used to approximate the unknown nonlinear functions. The addressed control scheme guarantees that the output is followed the reference signals within a bounded error, and all the signals in the closed-loop system are bounded. The simulation results demonstrate the validity of the proposed method.

This paper investigates the problem of decentralized H∞filtering for a class of interconnected Markovian jump systems with constant time delays. By utilizing a proper mode-dependent centralized Lyapunov functional together with the so-called Jensen's inequality, the sufficient conditions for the existence of both delay-dependent and mode-dependent decentralized filters are obtained, which guarantee that the filtering error system is stochastically stable with a prescribed H∞disturbance attenuation level. The obtained filters design conditions are expressed in terms of linear matrix inequalities (LMIs), which can be easily solved by a convex optimization software.