In this article, the event-triggered optimal bipartite consensus control problem is investigated for second-order discrete-time multiagent systems (MASs) with control input saturation and unknown system models. First, an instant reward signal with nonquadratic functions dealing with the control input saturation is defined, based on which a novel internal reinforce reward function is defined to facilitates agents to learn more intrinsic information from the local environment. Then, a novel event-triggered internal reinforce Q-learning (IrQL) algorithm is introduced. In contrast to conventional time-triggering Q-learning methods, the proposed event-triggered IrQL algorithm can not only fully exploit environment but also save the data computation and transmission resources. Based on elegant functional analysis techniques and Lyapunov stability theory, the internal reinforce reward function can be proved to be bounded and the tracking error dynamics of MASs are ensured asymptotic stability under the proposed event-triggered control policies. Then, data-driven reinforce-critic-actor neural networks are constructed to implement the event-triggered IrQL algorithm online with the proof of convergence. Finally, simulation examples show the validity and better performance over existing researches.

This article investigates the asynchronous estimator design for the interval type-2interval type-2 (IT2) fuzzy Markov jump systems subject to dynamic quantization and deception attacks. From the perspective of the attacker, a novel protocol-based deception attackdeception attack (DA) strategy is proposed, which utilizes the information of quantized output to assess the importance degree of transmission signals. Furthermore, in order to conserve the limited energy of the adversary, the independent attack strategies are designed for different sensors. Besides, the hidden Markov modelhidden Markov model (HMM) is applied to observe the system mode. Employing the Lyapunov stability theory and linear matrix inequality method, the sufficient conditions are acquired to guarantee the strictly-dissipative performance of the estimation error. Finally, two examples are illustrated to confirm the efficacy of the designed estimator and the advantage of the proposed attack tactics.

This article investigates secure bipartite containment control with the event-triggered mechanism (ETM) for nonlinear heterogeneous multiagent systems (MASs) via the feedback control in fixed time. A novel attacks detection algorithm and an updated label strategy are designed to judge the occurrence of Denial of Service (DoS) attacks or not. It can search for new multiple directed spanning trees for MASs with multiple leaders to decrease the negative influence when attacks happen. In this case, the dynamic ETM is adopted by adjusting the triggered threshold online to save resource consumption. The state-feedback control and output-feedback control are selectively employed according to the situation where the state value of the follower is available or not. The settling time is further relaxed by fixed-time stability theory and can be preset in advance. By theoretical discussion, conditions of achieving bipartite containment control are derived by Lyapunov functions. Finally, the effectiveness of the established control method is verified by simulations.

This article investigates the optimal bipartite consensus control (OBCC) problem for unknown second-order discrete-time multiagent systems (MASs). First, the coopetition network is constructed to describe the cooperative and competitive relationships between agents, and the OBCC problem is proposed by the tracking error and related performance index function. Based on the distributed policy gradient reinforcement learning (RL) theory, a data-driven distributed optimal control strategy is obtained to guarantee the bipartite consensus of all agents' position and velocity states. In addition, the offline data sets ensure the learning efficiency of the system. These data sets are generated by running the system in real time. Besides, the designed algorithm is an asynchronous version, which is essential to solve the challenge caused by the computational ability difference between nodes in MASs. Then, by means of the functional analysis and Lyapunov theory, the stability of the proposed MASs and the convergence of the learning process are analyzed. Furthermore, an actor-critic structure containing two neural networks is used to implement the proposed methods. Finally, a numerical simulation shows the effectiveness and validity of the results.

In this article, a novel model-free policy gradient reinforcement learning algorithm is proposed to solve the tracking problem for discrete-time heterogeneous multiagent systems with external disturbances over switching topology. The dynamics of the followers and the leader are unknown, and the leader's information is missing for each agent due to the switching topology. Therefore, a distributed adaptive observer is introduced to learn the leader's dynamic model and estimate its state for each agent. For the tracking problem, an exponential discount value function is established and the related discrete-time game algebraic Riccati equation (DTGARE) is derived, which is the key to obtaining the control strategy. Furthermore, a data-based policy gradient algorithm is proposed to approximate the solution of the GAREs online and the utilization of agents' accurate knowledge is avoided. To improve the efficiency of data utilization, an offline dataset and the experience replay scheme are used. In addition, the lower bound of the exponential discount value is explored to ensure the stability of the systems. In the end, a simulation is provided to show the validity of the proposed method.

This article is concerned with the input and output event-triggered control simultaneously for uncertain Euler–Lagrange systems with input saturation via backstepping technology. An auxiliary dynamic system is designed to eliminate the effects of saturation. Fuzzy logic systems are applied to the state observer design, which enables the observer to obtain satisfactory observation results without relying on the system dynamic parameters. The event-triggered weight adaptive law is designed to alleviate system's computational burden. Due to the existence of output triggering, conventional recursive backstepping technology is infeasible as the virtual control law is discontinuous and no longer differentiable. Consequently, dynamic surface control is introduced to circumvent the aforementioned problem. Compared with the existing literature that considers output triggering, the assumption of the system function satisfying the global Lipschitz continuity condition is relaxed in this article. Besides, variable transformation technology is used to ensure that the output is constrained within asymmetric performance functions. Finally, the simulation results are depicted to verify the validity of the derived method.

This article is devoted to dealing with exponential synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) under the framework of aperiodic sampling and state quantization. First, by taking the effect of aperiodic sampling and state quantization into consideration, a novel quantized sampled-data (QSD) controller with time-varying control gain is designed to tackle the exponential synchronization of INNs. Second, considering the available information of the lower and upper bounds of each HTVD, a refined Lyapunov-Krasovskii functional (LKF) is proposed. Meanwhile, an improved looped-functional method is utilized to fully capture the characteristic of practical sampling patterns and further relax the positive definiteness requirement for LKF. Consequently, less conservative exponential synchronization conditions with extra flexibility are derived. Finally, a numerical example is employed to demonstrate the effectiveness and advantages of the proposed synchronization method.

This article systematically studies the issue of adaptive neural network (NN) output-feedback control for uncertain nonlinear systems using event-triggered output. First, to tackle the problem of unmeasurable states, a compact state observer using event-triggered output is constructed. Then, since the event-triggered output signals are discontinuous, the virtual control laws in backstepping design are no longer differentiable. Hence, the dynamic surface control scheme is introduced to resolve this problem. Unlike existing work requiring system functions to satisfy Lipschitz continuity condition, adaptive NN control is incorporated into the designed algorithm to relax the above constraint. What is more, the event-triggered mechanism is also used for parameter estimation to avoid waste of computing and communication resources. Finally, the results of comparative simulations and the DC brush motor experiment are depicted to demonstrate the practicality and effectiveness of the proposed method.

This work investigates the problem of piecewise affine (PWA) filtering design for two-dimensional (2-D) Roesser nonlinear systems with finite frequency performance based on Takagi–Sugeno (T–S) fuzzy affine models. The goal is to synthesize a 2-D PWA filter such that the resulting filtering error system is asymptotically stable and simultaneously satisfies a finite frequency$\mathscr{H}_{\infty}$performance$\gamma$. With the utilization of the state space partition knowledge on 2-D fuzzy models, a novel PWA filter is obtained. By exploiting the 2-D Fourier transform technique to convert 2-D disturbances into their frequency domain counterparts, finite frequency$\mathscr{H}_{\infty}$performance analysis conditions are established, and then by applying projection lemma, an admissible frequency information-based filter design approach is proposed for 2-D Roesser nonlinear systems. Finally, the effectiveness of the PWA finite frequency filtering synthesis approach is validated through simulation studies on two examples.

In this article, the dynamic event-triggered control scheme is given to achieve the consensus of a class of nonlinear multiagent systems with switching topologies and random packet loss. Different from the existing works of modeling topology switching in a random way, the persistent dwell-time switching rule is utilized to depict the scenario of topology alterations. To cope with the problem of redundant data transmission, the information interaction between neighboring agents is determined by the up-front design triggering condition. Instead of the conventional static threshold parameter (TP) in the triggering condition, the TP in this article can be adjusted dynamically. This means that the update frequency of the controller can be further optimized. The imperfect matching phenomenon between premise variables about the fuzzy system and controller is also tackled. Moreover, in theory, the packet loss is modeled as a Markov process. Eventually, an application example about the truck-trailer model is presented to express the practicability of the proposed method.

In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.

This article tackles the problem of filtering design for continuous-time Roesser-type 2-D nonlinear systems via Takagi–Sugeno (T-S) fuzzy affine models. The aim is to design an admissible piecewise affine (PWA) filter such that the filtering error system is asymptotically stable with a prescribed disturbance attenuation level. First, 2-D Roesser nonlinear systems are approximated by a kind of 2-D fuzzy affine models with norm-bounded uncertainties. Then, the premise variable space of the 2-D fuzzy affine systems is partitioned into two classes of subspaces, that is: 1) crisp regions and 2) fuzzy regions. For each region, boundary continuity matrices and characterizing matrices are constructed by utilizing the space partition information and 2-D structure. After that, novel piecewise Lyapunov functions are constructed, based on which together with$S$-procedure, the asymptotic stability with$\mathcal H_{\infty }$performance is guaranteed for the filtering error system. By the projection lemma and some elegant convexification techniques, the PWA$\mathcal H_{\infty }$filtering design conditions are obtained. Finally, the less conservativeness and effectiveness of the proposed approach over a common Lyapunov function-based one are illustrated by simulation studies.

This article investigates the stability and stabilization problem of aperiodic sampled-data nonlinear systems in the framework of interval type-2 (IT-2) fuzzy models. First, by introducing two adjustable parameters and splitting the sampling intervals into four nonuniform intervals, a refined two-side looped-functional method is constructed to fully utilize inner state information during the whole aperiodic sampling interval. Simultaneously, the positive definiteness constraint for the individual matrix in Lyapunov–Krasovskii functional can be further relaxed. Then, via constructing a novel fuzzy Lyapunov–Krasovskii functional (FLKF) together with a fuzzy-dependent-switching scheme, the available features of fuzzy membership functions (FMFs) can be further taken into consideration to increase the design flexibility. Consequently, the stability condition and corresponding controller design approach for aperiodic sampled-data IT-2 fuzzy systems can be obtained with less design conservatism and larger sampling intervals. Finally, two simulation examples are employed to demonstrate the validity and superiority of the proposed method.

This article considers the problem of asynchronous fault detection filter (FDF) design for Takagi–Sugeno (T–S) fuzzy singular systems via dynamic event-triggered scheme. A mode-dependent dynamic event-triggered scheme is adopted to alleviate the communication load. Besides, a hidden Markov model is introduced to describe the asynchronous phenomenon between the system and the FDF. First, some sufficient criteria are established to ensure that the residual system is stochastically admissible with a certain$H_\infty$performance. Second, solvability criteria are presented to codesign the desired FDF gains and the event-triggered matrices. Finally, the correctness of the proposed method is shown by two examples.

This article studies the stability problem of generalized neural networks (GNNs) with time-varying delay. The delay has two cases: the first case is that the delay's derivative has only upper bound, the other case has no information of its derivative or itself is not differentiable. For both two cases, we provide novel stability criteria based on novel Lyapunov-Krasovskii functionals (LKFs) and new negative definite conditions (NDCs) of matrix-valued cubic polynomials. In contrast with the existing methods, in this article, the proposed criteria do not need to introduce extra state variables, and the positive-definite constraint on the novel LKF is relaxed. Moreover, based on free-matrix-based inequality (FMBI) and new NDCs, the stability conditions are expressed as linear matrix inequalities (LMIs). Eventually, the merits and efficiency of the proposed criteria are checked through some classical numerical examples.

This article is concerned with extended state observer (ESO) design for uncertain nonlinear systems. First, different from standard ESO exclusively applicable for integral chain systems, a ESO formation including nonlinear and linear types is proposed for general state-space models. Inspired by our previous work, a event-triggered generalized fuzzy ESO (GFESO) is developed. Second, in order to schedule transmission rationally, an improved total disturbance-resilient dynamic event-triggered mechanism (TDRDETM) is put forward. Third, under TDRDETM, the GFESO design approach is presented in sense of exponential convergence. Finally, numerical examples illustrate the effectiveness of the given methods.

In this paper, the consensus problem of linear discrete-time multi-agent systems is investigated. An event-triggered mechanism is introduced to rationally save the limited network resources, where the event-triggered condition of each agent only utilizes the information of itself and its neighbors. In the designed event-triggered mechanism, a novel dynamic threshold parameter adjusted with the system state fluctuations is constructed for screening the data more reasonably. With the help of Lyapunov stability theorem, the consensus conditions of the considered discrete-time multi-agent systems are obtained. To demonstrate the effectiveness and superiority of the proposed method, a numerical example is proposed.

This article studies the problem of finite frequency fault detection filtering design for uncertain nonlinear systems based on interval type-2 Takagi-Sugeno fuzzy models. It is assumed that the frequencies of disturbances and faults are in finite frequency sets, respectively. The objective is to design an admissible filter such that the fault detection system is asymptotically stable with prescribed finite frequency \mathscr H∞and \mathscr H-performances. Based on Fourier transform and Projection lemma, finite frequency filtering synthesis results are obtained. Then, a novel membership-function-dependent finite frequency fault detection filtering design approach is proposed by using the information of the lower and upper membership functions together with the footprint of uncertainties. Two algorithms with linear matrix inequality constraints are developed to optimize the finite frequency \mathscr H∞performance and the finite frequency \mathscr H-performance, respectively. Finally, simulation studies are provided to show the effectiveness of the proposed method.

This article studies the problem of finite-frequency static output feedback (SOF) \mathscr H∞controller design for discrete-time Roesser-type two-dimensional (2-D) nonlinear systems based on Takagi-Sugeno (T-S) fuzzy models. The 2-D Roesser nonlinear systems are described by T-S fuzzy models with parameter uncertainties. The objective is to design a SOF controller guaranteeing the asymptotic stability of the resulting closed-loop system with finite frequency \mathscr H∞performance. Via a system state-input augmentation technique, the closed-loop system is formulated in a descriptor form. Then, based on fuzzy Lyapunov functions and some elegant convexification procedures, the SOF controller design approach is proposed. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

This article is devoted to event-triggered stabilization for a class of interval type-2 (IT2) fuzzy systems with aperiodic sampling. First, the IT2 Takagi-Sugeno fuzzy model and sampled-data controllers are established subject to mismatched membership functions. Second, considering a nonuniform sampling case, a refined adaptive event-triggered communication scheme is proposed in a hierarchy form to dynamically adjust the direction and rate of the event-triggered threshold parameter by state changing trend and relative state error, respectively. Thus, a complete dual-directional regulating mechanism with sensitivity to state variation is reasonably created to give extra flexibility, which is beneficial for a preferable tradeoff between control performance and network resource. Third, considering the practical behaviors on the sampling interval, a novel integral type of time-dependent Lyapunov function is constructed. Then, the stability criterion and the controller design approach are derived. Finally, the numerical examples are provided to demonstrate the effectiveness and advantages of the proposed methods.

This article addresses the sampled-data piecewise affine (PWA) filter design problem for Itô stochastic nonlinear systems represented by Takagi–Sugeno fuzzy affine models. An input delay method is used to describe the sample-and-hold behavior of the measurement output. Based on a novel piecewise quadratic Lyapunov–Krasovskii functional, some new results on the robust sampled-data PWA filtering design are proposed through a linearization procedure by using some convexification techniques. Simulation studies on a tunnel diode circuit system, and an inverted pendulum system are given to illustrate the effectiveness of the proposed method.

This article focuses on the problem of fault detection filtering design for discrete-time interval type-2 Takagi-Sugeno (T-S) fuzzy systems in finite frequency domain. Considering the fact that external disturbances and faults are usually reside in finite frequency ranges, the finite frequency H∞and H-performances are introduced to reflect the disturbance robustness and fault sensitiveness in finite frequency domain, respectively. Based on discrete-time Fourier transform and its properties, finite frequency performance analysis results are first obtained. Then, by exploiting the information on upper and lower membership functions, the membership-function-dependent filtering design conditions in the form of linear matrix inequalities are established for discrete-time interval type-2 T-S fuzzy systems in finite frequency domain. With the obtained filter, a fault detection scheme is then proposed and it is shown that the resulting fault detection system is asymptotically stable with prescribed finite frequency H∞and H-performances. Finally, the effectiveness of the proposed method is validated by simulation studies.

This article considers the exponential stability and aperiodic sampled-data control problem for nonlinear systems based on a class of Takagi–Sugeno fuzzy models. The fuzzy-dependent-switching control strategy together with a novel time-varying sampled-data controller is proposed to deal with the exponential stabilization problem of such systems. Mixed-fuzzy dependent Lyapunov–Krasovskii functionals (MFDLKFs), which fully make use of available characteristics of the sampling patterns, the signs and the upper bounds of the time derivative of fuzzy membership functions, are constructed for the purpose of reducing the design conservatism. Based on the proposed MFDLKFs, a novel exponential stabilization criterion for the fuzzy systems with aperiodic sampling is established in terms of linear matrix inequalities, which is less conservative and obtains a larger sampling interval compared with existing results. Finally, a simulation example is employed to demonstrate the effectiveness and superiority of the proposed fuzzy-dependent-switching control scheme.

A dynamic event-triggered$\mathcal {H}_{\infty }$controller of the singular system based on stochastic cyber-attacks is studied in this article. To save network resources, a novel way is given, the cyber-attacks are considered as phenomena randomly occurring via network communication. First, sufficient conditions are derived, which ensure the closed-loop singular system to be regular, impulse free and asymptotically stable under a prescribed$\mathcal {H}_{\infty }$norm bound. Second, from some elegant linearization techniques, the method of design corresponding controller is put forward. Finally, some examples are given to illustrate the effectiveness of the obtained theoretical results.

This paper tackles the problem of piecewise affine memory filtering design for the discrete-time norm-bounded uncertain Takagi-Sugeno fuzzy affine systems. The objective is to design an admissible filter using past output measurements of the system, guaranteeing the asymptotic stability of the filtering error system with a given [Formula: see text] performance index. Based on the piecewise fuzzy Lyapunov functions and the projection lemma, a new sufficient condition for [Formula: see text] filtering performance analysis is first derived, and then the filter synthesis is carried out. It is shown that the filter gains can be obtained by solving a set of linear matrix inequalities. In addition, it is also shown that the filtering performance can be improved with the increasing number of past output measurements used in the filtering design. Finally, two examples are presented to show the advantages and effectiveness of the proposed approach.

In this paper, we will investigate the problem of finite frequency memory fixed-order output feedback controller design for Takagi–Sugeno (T–S) fuzzy affine systems. It is assumed that the disturbances reside in a finite frequency range, i.e., the low, middle, or high frequency range. The objective is to design a memory piecewise affine (PWA) controller by using past output measurements to guarantee the asymptotic stability of the resulting closed-loop system with a prescribed finite frequency $\mathscr H_{\infty }$ performance. Via the system state-input augmentation, a novel descriptor system approach is proposed to facilitate the controller design. All the design conditions are formulated in the form of linear matrix inequalities. It is also proven that the $\mathscr H_{\infty }$ performance can be improved with the memory control strategy. Finally, simulation studies are presented to show the effectiveness of the proposed design method.

In this study, the problem of filtering for sampled‐data systems under unreliable communication links is investigated. The phenomena of data packet dropouts and signal transmission delays are addressed in a unified framework. The objective is to design an admissible filter to guarantee the asymptotic stability of the filtering error system and minimise the disturbance attenuation level. Thanks to the proposed novel Lyapunov–Krasovskii functional together with the improved Wirtinger's inequality and the reciprocally convex approach, novel sufficient linear‐matrix‐inequality‐based conditions are obtained for the existence and design of admissible filters. Finally, two simulation examples are provided to illustrate the efficiency and less conservativeness of the proposed filter design methods.