This article studies the leader-following consensus problem for a class of linear multiagent systems over a directed graph with aperiodically sampled outputs. First, a novel distributed impulsive-observer-based consensus protocol is designed. This protocol requires only the output measurements at sporadic time instants for the observer and control gain design. Second, by using time-varying Lyapunov function techniques, sufficient conditions for exponential stability of a class of linear impulsive systems are established; subsequently, these stability results are applied for the distributed impulsive observer design. Different from the existing related works, the impulsive observer gain designed in this work is decoupled from the graph properties. As a result, once the impulsive observer gain is designed for one network topology, it can be directly used for other network topologies, as long as the graph properties and the dynamics of local agents satisfy certain conditions. Furthermore, the resilience of the designed protocol is tested under denial of service (DoS) attacks. It is shown that the protocol is robust with respect to low-frequency DoS attacks occurring in the observer communication network. Finally, two examples illustrating the validity and effectiveness of the proposed protocol are included.

In this study, the limitations of the tradeoff performance of multiple-input–multiple-output (MIMO) discrete networked control systems (NCSs) with forward channel subject to cyber-attack, additive white Gaussian noise and packet dropouts were analyzed. The performance of intrusion detection systems under cyber-attack with incomplete information was analyzed using game theory. Then, explicit expressions for the optimal tradeoff performance between tracking error and control input are derived based on the two-degree-of-freedom (TDOF) controller using frequency domain analysis, coprime factorization technique and Youla parameterization method. Results show that the tradeoff performance of the system is affected by their fundamental properties, such as the direction and position of the nonminimum phase (NMP) zeros and unstable poles (UPs) in the plant as well as communication constraints, such as cyber-attack, additive white Gaussian noise and packet dropouts. Finally, an illustrative simulation is discussed to verify the aforementioned conclusions.

This article revisits the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs) in the presence of disturbance in the input channel. A new Lyapunov approach based on double Lyapunov functionals is introduced for analyzing exponential input-to-state stability (EISS) of discrete impulsive delayed systems. In the framework of double Lyapunov functionals, a pair of timer-dependent Lyapunov functionals are constructed for impulsive DDNNs. The pair of Lyapunov functionals can introduce more degrees of freedom that not only can be exploited to reduce the conservatism of the previous methods, but also make it possible to design variable gain impulsive controllers. New design criteria for impulsive stabilization and impulsive synchronization are derived in terms of linear matrix inequalities. Numerical results show that compared with the constant gain design technique, the proposed variable gain design technique can accept larger impulse intervals and equip the impulsive controllers with a stronger disturbance attenuation ability. Applications to digital signal encryption and image encryption are provided which validate the effectiveness of the theoretical results.

This article investigates the leaderless consensus problem of discrete-time second-order nonlinear multiagent systems with the switching topology via the impulsive control. By defining a weighted average state associated the topology structure, two types of impulsive protocols are proposed: 1) dynamical consensus and 2) static consensus. The form drives all the agents toward the weighted average state, while the latter ensures that each agent tends to an agreed position. Unlike the existing impulsive protocols exerting impulsive actions on all the state variables of the agents, the proposed impulsive protocols only regulate the agents’ velocity vectors in an impulsive fashion throughout the consensus process. The introducing of the timer-dependent weighted double Lyapunov functions plays a key role in consensus analysis, which is able to exploit the relationship among the discrete-time agent’s dynamics, the impulsive protocols, and the communication topology. Sufficient conditions are derived to design the gain matrices of the impulsive protocols, which are dependent on the current impulse interval. The numerical results show that the variable gain-based design approach can tolerate larger variation in impulse intervals for impulsive consensus.

This article addresses the scaled consensus problem for a class of heterogeneous multiagent systems (MASs) with a cascade-type two-layer structure. It is assumed that the information of the upper layer state components is intermittently exchangeable through a strongly connected communication network among the agents. A distributed hierarchical hybrid control framework is proposed, which consists of a lower layer controller and an upper layer one. The lower layer controller is a decentralized continuous feedback controller, which makes the lower layer state components converge to their target values. The upper layer controller is a distributed impulsive controller, which enforces a scaled consensus for the upper layer state components. It is proved that the two layer controllers can be designed separately. By considering the dwell-time condition of impulses and the feature of the strongly connected Laplacian matrix, a novel weighted discontinuous function is constructed for scaled consensus analysis. By using the Lyapunov function, a sufficient condition for scaled consensus of the MAS is derived in terms of linear matrix inequalities. As an application of the proposed distributed hybrid control strategy, a relaxed distributed hybrid secondary control algorithm for dc microgrid is obtained, by which the balance requirement on the communication digraph is removed, and an improved current sharing condition is obtained.

This article investigates the asynchronous proportional-integral observer (PIO) design issue for singularly perturbed complex networks (SPCNs) subject to cyberattacks. The switching topology of SPCNs is regulated by a nonhomogeneous Markov switching process, whose time-varying transition probabilities are polytope structured. Besides, the multiple scalar Winner processes are applied to character the stochastic disturbances of the inner linking strengths. Two mutually independent Bernoulli stochastic variables are exploited to characterize the random occurrences of cyberattacks. In a practical viewpoint, by resorting to the hidden nonhomogeneous Markov model, an asynchronous PIO is formulated. Under such a framework, by applying the Lyapunov theory, sufficient conditions are established such that the augmented dynamic is mean-square exponentially ultimately bounded. Finally, the effectiveness of the theoretical results is verified by two numerical simulations.

This article aims to address the multiobjective scaled consensus problem for a class of heterogeneous multiagent systems (MASs) with communication delays: 1) a distributed hybrid control strategy is proposed which only requires local communication between neighbors to solve the consensus problem; 2) in order to overcome the technical difficulties caused by communication delays, a novel augmentation method is presented to model the error system with communication delays as a delay-free error system. A switching-based time-varying Lyapunov function is introduced to deal with switching impulses of the augmented system; 3) as an application of the proposed distributed control scheme, a new algorithm for designing the hybrid secondary control of the DC microgrid against communication delays is derived; and 4) the effectiveness of the proposed control scheme is illustrated by numerical examples and several case studies in islanded DC microgrid.

In this paper, a time-scale-dependent coupling scheme for two-time-scale nonlinear complex networks is proposed. According to this scheme, the inner coupling matrices are related to the fast dynamics of individual subsystems, but are no longer time-scale-independent. Designing time-scale-dependent inner coupling matrices is motivated by the fact that the difference of time scales is an essential feature of modular architecture of two-time-scale systems. Under the novel coupling framework, the previous assumption on individual two-time-scale subsystems that the fast dynamics must be exponentially stable can be removed. The idea of time-scale separation is employed to analyze the stability of synchronization error systems via weighted ε -dependent Lyapunov functions. For a given upper bound of the singular perturbation parameter ε , it is proved that the exponential decay rate of the synchronization error can be guaranteed to be independent of the value of ε . In this way, criteria for local and global exponential synchronization are established. The allowable upper bound of ε such that the synchronizability of the considered two-time-scale network is retained can be obtained by solving a set of ε -dependent matrix inequalities. Finally, the efficiency of the proposed time-scale-dependent coupling strategy is demonstrated through numerical simulations.

This paper is dedicated to designing periodic impulsive control strategy for generating globally stable periodic solutions for periodic neural networks with discrete and unbounded distributed delays when such neural networks do not have stable periodic solutions. Two criteria for the existence of globally exponentially stable periodic solutions are developed. The first one can deal with the case where no bounds on the derivative of the discrete delay are given, while the second one is a refined version of the first one when the discrete delay is constant. Both stability criteria possess several adjustable parameters, which will increase the flexibility for designing impulsive control laws. In particular, choosing appropriate adjustable parameters can lead to partial state impulsive control laws for certain periodic neural networks. The proof techniques employed includes two aspects. In the first aspect, by choosing a weighted phase space PCα, a sufficient condition for the existence of a unique periodic solution is derived by virtue of the contraction mapping principle. In the second aspect, by choosing an impulse-time-dependent Lyapunov function/functional to capture the dynamical characteristics of the impulsively controlled neural networks, improved stability criteria for periodic solutions are attained. Three numerical examples are given to illustrate the efficiency of the proposed results.

The bipartite consensus problem of multiple two-time scales agents over the signed digraph, where both cooperative and competitive interactions exist among the agents, is considered with a new distributed protocol. Sufficient conditions for bipartite consensus is presented in terms of easily checkable algebraic Riccati equation (ARE). Compared with the existing result on consensus of multiple two-time scales agents, the communication topology here is more generic. Moreover, the upper bound of the singular perturbation parameter is also presented. Simulation examples are given to illustrate the effectiveness of the proposed results.

This paper is concerned with impulsive stabilization of periodic solutions of recurrent neural networks (RNNs) with discrete and distributed delays. By considering two different types of bounded discrete-delays, two stability criteria are formulated respectively for the case where the information on the discrete-delay derivative is unknown and the case where the discrete-delay derivative be strictly less than one. It is shown that the first stability criterion is an essential improvement over the existing one in the literature. When the discrete-delay is constant, the second stability criterion is proved to be less conservative than the first stability criterion. Moreover, impulsive control law design for delayed RNNs is facilitated by adjustable parameters in the stability criteria. The usefulness of the theoretical findings is demonstrated by a numerical example.

This paper presents a new impulsive synchronization criterion of two identical reaction-diffusion neural networks with discrete and unbounded distributed delays. The new criterion is established by applying an impulse-time-dependent Lyapunov functional combined with the use of a new type of integral inequality for treating the reaction-diffusion terms. The impulse-time-dependent feature of the proposed Lyapunov functional can capture more hybrid dynamical behaviors of the impulsive reaction-diffusion neural networks than the conventional impulse-time-independent Lyapunov functions/functionals, while the new integral inequality, which is derived from Wirtinger's inequality, overcomes the conservatism introduced by the integral inequality used in the previous results. Numerical examples demonstrate the effectiveness of the proposed method. Later, the developed impulsive synchronization method is applied to build a spatiotemporal chaotic cryptosystem that can transmit an encrypted image. The experimental results verify that the proposed image-encrypting cryptosystem has the advantages of large key space and high security against some traditional attacks.

This paper investigates the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs). Two types of DDNNs with stabilizing impulses are studied. By introducing the time-varying Lyapunov functional to capture the dynamical characteristics of discrete-time impulsive delayed neural networks (DIDNNs) and by using a convex combination technique, new exponential stability criteria are derived in terms of linear matrix inequalities. The stability criteria for DIDNNs are independent of the size of time delay but rely on the lengths of impulsive intervals. With the newly obtained stability results, sufficient conditions on the existence of linear-state feedback impulsive controllers are derived. Moreover, a novel impulsive synchronization scheme for two identical DDNNs is proposed. The novel impulsive synchronization scheme allows synchronizing two identical DDNNs with unknown delays. Simulation results are given to validate the effectiveness of the proposed criteria of impulsive stabilization and impulsive synchronization of DDNNs. Finally, an application of the obtained impulsive synchronization result for two identical chaotic DDNNs to a secure communication scheme is presented.

In this paper, the problem of exponential stability for Takagi-Sugeno (T-S) fuzzy delay systems with delayed impulses is addressed. By means of the time-dependent Lyapunov function method combined with Razumikhin technique, a sufficient condition expressed in terms of linear matrix inequalities (LMIs) is obtained for exponential stability of T-S fuzzy delay systems with delayed impulses. The derived stability condition depends both on the lower bound and the upper bound of impulsive intervals, which is robust with respect to small impulse input delays. By solving a set of LMIs, an impulsive state feedback controller can be easily constructed. Two numerical examples are discussed to illustrate the effectiveness of the theoretical findings.

This paper is concerned with analyzing mean square exponential stability of stochastic delayed neural networks subject to parametric uncertainties. The discretized Lyapunov functional technique is first utilized to construct a new Lyapunov functional in order to effectively deal with the time-varying delay. Then the free-weighting matrix technique and the convex combination method are used to establish a new delay-dependent mean square exponential stability criterion for uncertain stochastic delayed neural networks. The usefulness of the new theoretical findings is further demonstrated by numerical results.

This brief investigates the problem of mean square exponential stability of uncertain stochastic delayed neural networks (DNNs) with time-varying delay. A novel Lyapunov functional is introduced with the idea of the discretized Lyapunov-Krasovskii functional (LKF) method. Then, a new delay-dependent mean square exponential stability criterion is derived by applying the free-weighting matrix technique and by equivalently eliminating time-varying delay through the idea of convex combination. Numerical examples illustrate the effectiveness of the proposed method and the improvement over some existing methods.

This paper presents new complete stability results for delayed cellular neural networks (DCNNs). A novel method is proposed for complete stability analysis of DCNNs. By applying the M-matrix theory and introducing some new estimation techniques on the solutions of DCNNs, a simple and improved complete stability criterion is derived. The new criterion unifies the delay-dependent and delay-independent complete stability conditions for DCNNs. Moreover, the obtained delay-dependent criterion can give a larger upper bound of the time delay than the existing ones such that the complete stability can still be retained. Numerical examples are presented which show that the new complete stability results for DCNNs are compared favorably with the existing results.

The problem of global exponential stability analysis of impulsive neural networks with variable delays is investigated in this paper. The cases of impulses considered in this paper include that (1) the impulses are input disturbance; (2) the impulses are “neutral” type (that is, they are neither helpful for stability for neural networks nor destabilizing). A new approach based on Lyapunov function and Razumikhin-type techniques is developed to establish delay-independent sufficient conditions for global exponential stability in each case of impulses. These new stability conditions are expressed in form of linear matrix inequalities with regard to proper types of impulse time sequences and are independent of the size of variable delays. The effectiveness of the new results are further illustrated by numerical examples.

This brief is concerned with asymptotic stability of neural networks with uncertain delays. Two types of uncertain delays are considered: one is constant while the other is time varying. The discretized Lyapunov-Krasovskii functional (LKF) method is integrated with the technique of introducing the free-weighting matrix between the terms of the Leibniz-Newton formula. The integrated method leads to the establishment of new delay-dependent sufficient conditions in form of linear matrix inequalities for asymptotic stability of delayed neural networks (DNNs). A numerical simulation study is conducted to demonstrate the obtained theoretical results, which shows their less conservatism than the existing stability criteria.

This paper addresses the problem of complete stability analysis for cellular neural networks (CNNs) with variable delays. The M-matrix theory and new analysis techniques are utilized to establish novel delay-independent/delay-dependent sufficient conditions under which the complete stability of CNN's with variable delays can be guaranteed. The new stability criteria do not require any symmetric condition of the feedback matrix and the delayed feedback matrix, thus being much widely applicable. The developed theoretical results are further illustrated by numerical examples, including their superiority over the existing results.

The problems of existence, uniqueness and global exponential stability of the equilibrium of Cohen-Grossberg neural networks with time-varying delays are investigated in this paper. A new approach is developed to establish delay-independent/dependent sufficient conditions for global exponential stability. The results obtained can be easily checked in practice and do not require the delays to be constant or differentiate. In particular, our delay-dependent exponential stability conditions give explicitly the allowable upper bound of the delays that guarantees stability of Cohen-Grossberg neural networks, and are applicable to the case when the non-delayed terms cannot dominate the delayed terms. The effectiveness of the new results are further illustrated by numerical examples in comparison with the existing results.