This article investigates the limited-time bipartite synchronization (LTBS) problem of coupled T-S fuzzy memristive neural networks (CTSFMNNs) with antagonistic interactions, where LTBS covers both fixed-time bipartite synchronization (FXTBS) and preassigned-time bipartite synchronization (PATBS). To address this issue, a fuzzy exponential control strategy is developed based on an improved adaptive event-triggered mechanism (IAETM). By employing constant gains and exponential terms instead of conventional switching gains and multiple power-law structures, the controller design is simplified and its implementability is enhanced. Furthermore, an adaptive function and two dynamic regulation terms are incorporated into the ETMs to adjust the triggering threshold according to measurement-error variations, thereby reducing controller updates and conserving communication resources. Then, algebraic inequality criteria are established through Lyapunov theory and the fuzzy set method to guarantee the LTBS of CTSFMNNs. Finally, numerical simulations are conducted to verify both the effectiveness of the derived synchronization conditions and the feasibility of an image data protection framework for Pantograph-Catenary Monitoring System (PCMS) constructed from the obtained LTBS theory. The proposed framework provides controllable key-generation time, a decoupled design of the key and the synchronous sequence, and enhanced resistance to structure-inference attacks, thereby improving security and demonstrating strong engineering potential.

The leader-following consensus issue for general linear multiagent systems (MASs) is investigated under event-triggered communication (ETC). Based on the output measurement of agent, two novel adaptive dynamic event-triggered (ADET) strategies for synchronous and asynchronous ETC are proposed, in which adaptive triggering parameters and time-varying threshold are introduced. Simultaneously, the corresponding control protocols are developed under the ADET strategies. Different from most existing relevant results, triggering mechanisms and control protocols do not require global information and network size, ensuring a fully distributed implementation. The asynchronous ETC, in particular, gives a flexible for transmitting information. The effectiveness of ADET strategies is further analyzed and discussed, and a comparative analysis between observer-based and state-based ADET algorithms is provided, highlighting their efficiencies and applications. Theoretical claims are substantiated with a simulative example that demonstrates the practical effectiveness of the proposed strategies.

This article studies the stability issue and provides the hierarchical stability conditions for generalized neural networks (GNNs) embedded with interval variant delay (delay’s differential is unidentified). First, by transforming the state vectors with integral in the generalized free-matrix-based integral inequalities (GFIIs) into the multiple integral state vectors, the Lyapunov-Krasovskii functional (LKF) with hierarchy is put up based on these multiple integrals. Then, in the treatment of the LKF derivative, the GFIIs are utilized to estimate the delay related integrals of the quadratic product items. For the LKF differential, it is obtained as the delay function with the$2N-1$degree. Next, to set up the linear matrix inequality (LMI) forms and solve the nonlinear items injected by the GFIIs, the novel matrix-based negative conditions (NCs) for odd degree polynomials are put forward. Finally, the superiority of the proposed stability conditions with hierarchy is illustrated by several numerical examples.

In this article, the stability analysis for generalized neural networks (GNNs) with a time-varying delay is investigated. About the delay, the differential has only an upper boundary or cannot be obtained. For the both two types of delayed GNNs, up to now, the second-order integral inequalities have been the highest-order integral inequalities utilized to derive the stability conditions. To establish the stability conditions on the basis of the high-order integral inequalities, two challenging issues are required to be resolved. One is the formulation of the Lyapunov-Krasovskii functional (LKF), the other is the high-degree polynomial negative conditions (NCs). By transforming the integrals in N-order generalized free-matrix-based integral inequalities (GFIIs) into the multiple integrals, the hierarchical LKFs are constructed by adopting these multiple integrals. Then, the novel modified matrix polynomial NCs are presented for the 2N-1 degree delay polynomials in the LKF differentials. Thus, the hierarchical linear matrix inequalities (LMIs) are set up and the nonlinear problems caused by the GFIIs are solved at the same time. Eventually, the superiority of the provided hierarchical stability criteria is demonstrated by several numeric examples.

In this article, we study the matrix-weighted consensus issues for second-order discrete-time multiagent systems on directed network topology. Under the designed matrix-weighted consensus algorithm, based on the eigenvalues of the Laplacian matrix, coupling gains, and discrete interval, we build some consensus conditions for reaching discrete-time consensus and deduce some simplified and straightforward consensus conditions for undirected network topology. Besides, for a given network topology, we theoretically analyze the influence of the coupling gains and discrete intervals on the consensus conditions of the network dynamics. Finally, we offer several simulation examples to validate the obtained results.

In this article, we consider the cooperative output regulation for linear multiagent systems (MASs) via the distributed event-triggered strategy in fixed time. A novel fixed-time event-triggered control protocol is proposed using a dynamic compensator method. It is shown that based on the designed control scheme, the cooperative output regulation problem is addressed in fixed time and the agents in the communication network are subject to intermittent communication with their neighbors. Simultaneously, with the proposed event-triggering mechanism, Zeno behavior can be ruled out by choosing the appropriate parameters. Different from the existing strategies, both the compensator and control law are designed with intermittent communication in fixed time, where the convergence time is independent of any initial conditions. Moreover, for the case that the states are not available, the output regulation problem can further be addressed by the distributed observer-based output feedback controller with the fixed-time event-triggered compensator and event-triggered mechanism. Finally, a simulation example is provided to illustrate the effectiveness of the theoretical results.

In this article, we focus on the problems of consensus control for nonlinear uncertain multiagent systems (MASs) with both unknown state delays and unknown external disturbances. First, a nonlinear function approximator is proposed for the system uncertainties deriving from unknown nonlinearity for each agent according to adaptive radial basis function neural networks (RBFNNs). By taking advantage of the Lyapunov-Krasovskii functionals (LKFs) approach, we develop a compensation control strategy to eliminate the effects of state delays. Considering the combination of adaptive RBFNNs, LKFs, and backstepping techniques, an adaptive output-feedback approach is raised to construct consensus tracking control protocols and adaptive laws. Then, the proposed consensus tracking scheme can steer the nonlinear MAS synchronizing to the predefined reference signal on account of the Lyapunov stability theory and inequality properties. Finally, simulation results are carried out to verify the validity of the presented theoretical approach.

This article investigates the dissipative stability and stabilization problems of Takagi–Sugeno fuzzy systems by employing sampled-data control. First, in order to enhance the adaptability of the controller, the internal and external influence factors, such as communication time delay and aperiodic sampling pattern, are taken into consideration in the design process. Then, some new terms are proposed to construct the looped-functional-like Lyapunov functional, which can improve the dissipative performance, enlarge the admissible upper bound of the aperiodic sampling periods, and reduce the occupation of control signals in the communication channel. Next, based on the free-matrix-based integral inequalities, some novel dissipative criteria are presented in terms of linear matrix inequalities. Finally, the superiority of the provided criteria is demonstrated through a truck–trailer system.

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.

The leader-following exponential output consensus (LFEOC) problem of nonlinear fractional-order MASs (FOMASs) with unknown mismatched disturbances and event-triggered control (ETC) input is investigated. First, a finite-time higher-order sliding observer is constructed for the estimation of the unknown matched and mismatched disturbances of each agent. Second, a distributed integral sliding-mode (ISM) surface is established to realize exponential stabilization of FOMASs, and a distributed event-based active anti-disturbance consensus protocol is designed to compensate the effects of unmatched uncertainties on the output and save energy. Third, it is proved that the output of all agents can be driven to achieve exponential consensus without Zeno behavior, and the corresponding event-triggered conditions are presented. Finally, the efficacy of conclusions is verified by the examples.

This article focuses on the problem of adaptive scaled consensus tracking control for uncertain nonlinear multiagent systems (MASs) subjected to unknown mixed control gains, input delays, and external disturbances. First, a new form of nonlinear MAS is presented by linear state transformation. Second, a state observer based on adaptive radial basis function neural networks is developed to estimate the unmeasured states. A command filter control scheme is deployed to address the problem of increased sharply complexity derived from the conventional backstepping design with the increase of the system order, and the filtered error is compensated by the error compensation mechanism. Third, by taking advantage of the Lyapunov–Krasovskii functionals, a compensation control strategy is intended to exclude the impact of input delays. In addition, a Nussbaum-gain function is used to deal with the problem of uncertain control direction. An adaptive output feedback control approach is raised to construct the scaled consensus tracking control protocol, error compensating signals, and adaptive laws. It is proved that the tracking errors are driven to a small residual set, and all the signal variables are bounded in the closed-loop system. Finally, the effectiveness of the proposed approach is verified by two numerical simulations.

In this article, the${H_{\infty } }$scaled consensus control problem for multiagent systems in the presence of external disturbances and mixed time delays in both input and Lipschitz nonlinearity is investigated. First, a state observer is introduced for each agent based on the output information of the agent. Then, a scaled consensus protocol is proposed via a truncated predictor output-feedback method, which can deal with the input delay. The integral terms with the mixed time delays that are contained in the transformed systems are analyzed by using the Lyapunov–Krasovskii functionals method, and sufficient conditions are obtained to achieve scaled consensus with guaranteed${H_{\infty } }$performance. An iterative procedure is utilized to calculate the linear matrix inequality. By this, the feedback gain and observer gain are then designed. Finally, a simulation example is provided to illustrate the effectiveness of the theoretical results.

Sampled-data state feedback control with stochastic sampling periods for Boolean control networks (BCNs) is investigated in this article. First, based on the algebraic form of BCNs, stochastic sampled-data state feedback control is applied to stabilize the considered system to a fixed point or a given set. Two kinds of distributions of stochastic sampling periods are considered. First, the distribution of sampling periods is assumed to be independent identically distributed (i.i.d.) in the range of any positive integers and the second distribution of sampling periods is assumed to follow an infinite Markov process. A BCN with infinite stochastic sampling periods proves to be equivalent to a finite stochastic switched system, based on which, necessary and sufficient conditions are given to guarantee the stabilization and set stabilization of the BCN with stochastic sampling periods. For the first one, two algorithms are given to guarantee the stabilization and set stabilization of the considered system. For the second one, necessary and sufficient conditions are all presented in the linear programming form. Examples are listed to show the effectiveness of our results.

This article focuses on scaled consensus tracking for a class of high-order nonlinear multiagent systems. Different from the existing results, for high-order nonlinear multiagent systems with time delays and external disturbances, a fully distributed consensus protocol is designed to drive all agents to achieve scaled consensus with preassigned ratios. The control gains are varying and updated by distributed adaptive laws. As a result, the presented protocol is independent of any global information, and thus, could be implemented in a fully distributed manner. Simultaneously, the fully distributed control protocol using an adaptive$\sigma$-modification technique is presented to deal with external disturbances, which can guarantee the tracking errors and coupling weights of all following agents are uniformly ultimately bounded. To tackle with the derivatives of the functionals with time delays, the Lyapunov–Krasovskii functional is employed to analyze and compensate them by introducing multiintegral terms. Finally, simulation examples are included to verify the effectiveness of the theoretical results.

In this article, the problems of inverse-optimal consensus control (IOCC) for single-integral dynamics in Caputo continuous-time and Grunwald–Letnikov (G-L) discrete-time fractional-order multiagent systems (FOMASs) are investigated. For continuous-time FOMASs, by virtue of the variational method, the optimal solution under the constraint of the global performance index function is presented, and then the optimal state-feedback gain matrix (OSFGM) is derived through the Laplace transform and the inverse transform of fractional order. For discrete-time FOMASs, the OSFGM based on the algebraic Riccati equation (ARE) is computed, then it is shown that the OSFGM is a (asymmetric) Laplace matrix which has a zero eigenvalue and the corresponding network structure is a complete graph. Moreover, we rigorously prove that consensus can be achieved under the optimal state-feedback gain matrices. Finally, two simulation examples are shown to check the validity of the proposed theorems.

In this article, the consensus problem of linear multiagent systems (MASs) is investigated, where an event-triggered mechanism combined with sampler is introduced to rationally utilize the network bandwidth and the communication energy. Different from most existing results on the event-triggered consensus of MASs, a time-varying communication delay (CD) with a more general form is constructed in this article. With the help of a new class of Lyapunov functional and advanced inequalities, some novel criteria for achieving the asymptotic consensus of the considered MASs under different CD cases are obtained. For demonstrating the superiority of the proposed method from the conservatism and the practicability, a compared example and an application of spacecraft formation flying are proposed, respectively.

This article is concerned with the event-triggered guaranteed cost control for a class of Markovian jump systems with time-varying delays and partly unknown transition probabilities, which is described by the Takagi-Sugeno fuzzy model. For the sake of saving network bandwidth, an event-triggered mechanism related to system modes is developed in the feedback channel, in which network-induced delay randomly occurs. The stability criterion for the system with a guaranteed cost index is derived by the Lyapunov-Krasovskii functional. Moreover, sufficient conditions that ensure the existence of the admissible fuzzy controllers are given. By means of the proposed approach, the fuzzy control gains and event-triggered parameters can be codesigned. Finally, some simulation results are presented to demonstrate the superiority and effectiveness of the developed method.

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.

This paper is concerned with the sliding mode control problem for a class of continuous-time switched neural networks with mode-dependent average dwell time (MDADT). The considered continuous-time switched neural networks are motivated by biological neural networks which contain a nonlinear term and a changeable switched signal. The concept of MDADT is introduced, in which every subsystem has its own dwell time before switching to another subsystem. Moreover, a novel sliding mode controller is designed by an event-triggered mechanism which is based on the observer error and the system mode, where its triggered condition can be more conservative and practical than the existing triggered conditions. Sufficient conditions are derived to ensure that the closed-loop system is stochastically exponentially stable in terms of linear matrix inequalities. The designed sliding mode controller can promote the sliding mode motion of the system state. Finally, an illustrative example is provided to demonstrate the effectiveness and merits of the proposed method.

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 is concerned with the consensus tracking problem for heterogeneous interdependent group systems with fixed communication topologies. First, the interdependent model of the heterogeneous system is built from the perspective of the difference of the individual characteristic and the difference of the subgroup topology structure. A class of distributed consensus tracking control protocol is proposed for realizing the consensus tracking of the heterogeneous interdependent group system via using local information. Then, for fixed communication topologies, some corresponding sufficient conditions are given to ensure the achievement of the consensus tracking. Two parameters are defined, which denote, respectively, the proportion of interdependence individual and the redundancy of interdependence. The effects of these parameters are analyzed on the consensus tracking of group systems. Numerical simulations are provided to illustrate the effectiveness of the theoretical analysis.

In this paper, the scaled consensus of multiagent systems (MASs) with second-order nonlinear dynamics and time-varying delays is investigated, where agents are supposed to aperiodically communicate with each other under a directed graph at some disconnected time intervals. Different from the existing works, we consider the case where time-varying delays exist in both nonlinear dynamics and communication networks. First, to address this problem, we propose a novel scaled consensus protocol. Second, using Lyapunov theory and graph theory, it is proved that under mild conditions, MASs with second-order nonlinear dynamics exponentially reach scaled consensus. Moreover, we extend our results to the case of leader-following scaled consensus. Finally, the simulation examples are included to verify the effectiveness of the theoretical results.

In this paper, the flocking behavior and targets consensus tracking problems of heterogeneous multiple inertial agents with limited communication ranges are investigated. Consider the inertial effect of real agents, a distributed control protocol is designed such that the agents can achieve stable group behaviors. Using the decomposition approach, the stability of a multiple inertial agents system is proved. Combining with the flocking behavior of multiple inertial agents, agents themselves are in charge of searching and choosing the target in an autonomous and individual way according to the relationship between velocities of agents and targets. An augmented distributed multiflocking method is proposed to guarantee that the multitarget consensus tracking can be reached for heterogeneous multiagent systems. It shows that the proposed control approach can not only ensure local flocking with pursuing agents, but also make heterogeneous agents consistently track the multitarget.

This paper considers the problem of distributed H∞filtering for a class of switched repeated scalar nonlinear systems with randomly occurred sensor nonlinearities and asynchronous switching due to practical reasons. The possibility of randomly occurred sensor nonlinearities is described by a Bernoulli stochastic variable, and the asynchronous switching filtering means that the mode of the plant is different from the mode of the designed filter possibly. A distributed filtering network is used to estimate the system state instead of a filter to improve reliability in case of faults of the filter. A distributed mode-dependent filter is designed by constructing a unified mode-dependent Lyapunov function and solving a set of linear matrix inequalities. Some novel sufficient conditions are obtained by using the average dwell time switching mechanism such that the augmented filtering error system is stochastically exponentially stable and achieves a prescribed H∞disturbance attention index. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed designed method.

This paper studies a class of fast consensus algorithms for a group of identical multiagent systems each described by the linear state-space model. By using both the current and delayed state information, the proposed delay-induced consensus algorithm is shown to achieve synchronization with a faster convergence speed than the standard one when the eigenvalues of the open-loop system, control parameters, the Laplacian matrix of the network, and the delay satisfy certain conditions. In addition, some sufficient or necessary and sufficient conditions are established to guarantee the closed-loop stability of the delay-induced consensus algorithm, where an extra control parameter on the coupling strength is introduced to adjust the convergence speed of the closed-loop system flexibly. We then show that the delay-induced algorithm is robust to the small intrinsic communication or input delays, i.e., the proposed delay-induced consensus algorithm may also produce a faster convergence speed than the standard one even if there exist small intrinsic communication or input delays. Furthermore, we extend the results from the case of an undirected communication topology to those of a directed communication topology and a switching communication topology. Several simulation examples are presented to illustrate the theoretical results.