This article addresses the problem of finite-time filtering for a class of discrete-time Takagi-Sugeno (T-S) fuzzy-model-based networked control systems (NCSs) subject to switching communication channels and cyber attacks. Specifically, the fluctuations of the state estimator gain parameters are taken into account in the design of the filter. Further, cyber attacks are supposed to take place in the communication channel and can modify the sensor signals by injecting false data if an attack is successful. Meanwhile, the transition probability of the Markov process is a switching phenomenon along with the minimum and maximum bounds of the multichannel communication scheme. By employing appropriate Lyapunov–Krasovskii functional along with reciprocally convex approach, a set of sufficient conditions guaranteeing the finite-time mixed$H_\infty$and passivity performance for NCSs are derived in terms of linear matrix inequalities. In the end, two illustrated examples quantify the effectiveness of the proposed analysis and filter design method.

This article investigates the asymptotic stabilization of periodic piecewise time-varying systems with time-varying delay under various cyber attacks, particularly deception and acrlong DoS attacks. The addressed system is reformed into a number of time-varying subsystems based on the time interval for each period. Following that, a state-feedback controller with periodic time-varying gain parameters is developed to solve the stabilization problem. The control design depicts the possibility of the aforementioned cyber attacks with two mutually exclusive stochastic Bernoulli distributed parameters. Then, an augmented Lyapunov-Krasovskii functional with periodically varying matrices is used to determine the conditions for designing the proposed controller that ensures the mean-square asymptotic stability of the addressed system. The results of numerical examples support the conclusion that the proposed method is effective and superior, regardless of the cyber attacks involved.

In this article, a synchronization method of complex dynamical networks (CDNs) with time-varying delay feedback control is proposed to secure communication between the command system and each node of CDNs. To advance the security of the communication between the command system and each node of CDNs, the original information signal transmitted from the command system is encrypted with the techniques of$\mathcal {N}$-shift cipher and public key. Based on the Lyapunov stability sense and linear matrix inequality (LMI) framework, a new delay-dependent synchronization criterion is established to restore the original information signal on each node of the CDN as well as to ensure stable synchronization for secure communication of all nodes of the command system and CDN. With the support of numerical simulation, the validity of the proposed method is verified.

This article aims to resolve the event-based finite-time dissipative filtering issue for interval type-2 fuzzy complex dynamical networks with coupling delays and cyber attacks. An event-triggered scheme introducing the estimation error is designed, which is a tradeoff between state estimator performance and network communication bandwidth in accordance with the practical requirements. Moreover, the cyber attack model is established, which consists of randomly occurring deception attacks. The stochastic variable obeying the Bernoulli distribution is utilized to describe the attack condition. Through handling the linear matrix inequalities (LMIs) with some slack matrices, the event-triggered finite-time filter is designed to guarantee that the resulting systems are finite-time bounded and strictly dissipative. The proposed filter design parameters are obtained by solving LMIs. Finally, a numerical example and a practical example of a tunnel diode circuit system are used to show the effectiveness and applicability of the proposed event-based dissipative filtering scheme.

This article proposes three new methods to enlarge the feasible region for guaranteeing stability for generalized neural networks having time-varying delays based on the Lyapunov method. First, two new zero equalities in which three states are augmented are proposed and inserted into the results of the time derivative of the constructed Lyapunov-Krasovskii functionals for the first time. Second, inspired by the Wirtinger-based integral inequality, new Lyapunov-Krasovskii functionals are introduced. Finally, by utilizing the relationship among the augmented vectors and from the original equation, newly augmented zero equalities are established and Finsler's lemma are applied. Through three numerical examples, it is verified that the proposed methods can contribute to enhance the allowable region of maximum delay bounds.

The issues of input–output finite-time stability and stabilization of Takagi–Sugeno (T–S) fuzzy systems with time-varying state delay and exogenous input signal are studied in this article. For the stabilization process, a controller that does not share the same membership functions as the system is considered and the state feedback is quantized by using a logarithmic quantizer. The desired stability criterion for the addressed fuzzy system is first derived without the control term. It is then extended to the case in which the proposed controller is present. The stability criteria are given in the form of matrix inequalities, which are supported by the augmented Lyapunov–Krasovskii functional, generalized free-weighting-matrix approach and Finsler’s lemma. Moreover, as a special case, an asymptotic stability criterion is obtained and used in a comparative study. As a result, the time-delay interval is much larger than some of the works published very recently, which is due to the augmented Lyapunov–Krasovskii functional construction. In addition, the effectiveness and practicability of the theoretical findings are validated with simulation examples.

In this article, the robust asynchronous filtering problem is investigated for a class of discrete-time T–S fuzzy complex dynamical networks subjected to random coupling delays and deception attacks. Specifically, the deception attacks are considered in the filter, where the adversary attempts to inject some false information data in the measurement output to modify the transmitted signal in the communication networks. We introduce, respectively, two sets of stochastic variables satisfying the Bernoulli distribution to depict the probability of the data transmitted by the network being subjected to time-varying coupling delays and deception attacks. By using Lyapunov–Krasovskii stability theory and Abel lemma-based finite-sum inequality, a new set of sufficient conditions is established, which ensures the stochastic stability of the resulting error system with a prescribed mixed$H_\infty$and passivity performance index. The proposed filter parameters are obtained by solving linear matrix inequalities. Ultimately, both the effectiveness and advantages of the proposed asynchronous filter with deception attacks are verified by two numerical examples including a tunnel diode circuit model.

In this work, the problem of fault and state estimation is studied for Takagi–Sugeno fuzzy systems with time-varying delay and external disturbance over a finite horizon. A fuzzy rule-based fault estimator with two tuning parameters, including the current system output information, is designed. Based on which, the Luenberger state observer is then considered to estimate the immeasurable states of the system addressed. The aim of taking into account the tuning parameters in the estimator design is to improve the accuracy of the fault estimation. More precisely, by defining error variables in accordance with actual and estimated system states and faults, an augmented system is formulated to achieve the desired results. By using the existing stability theory and integral inequality, a delay-dependent criterion for ensuring that the states of the augmented system are bounded over a finite horizon is derived. Subsequently, conditions for determining the observer gain matrices are presented. Two simulation examples, including an application example of the truck-trailer model, are provided to demonstrate the advantages of the theoretical results that have been established. In particular, the importance of the use of tuning parameters is discussed.

This article intends to provide an integrated robust synchronization and anti-disturbance control design for a nonlinear multiweighted complex network with the use of fuzzy modeling approach. The network takes the effect of both matched and mismatched disturbances into account, wherein the matched one is unknown and caused by some exogenous systems. In order to estimate the unknown disturbance, a disturbance observer is precisely considered. By exploiting the parallel distributed compensation strategy and the output of disturbance observer, the desired fuzzy rule-based control law is formulated. Based on these settings, the required control gain matrices that affirm asymptotic synchronization of the addressed network are determined with the support of the Lyapunov functional method and extended Wirtinger's integral inequality. The method of this article can render better system performance than the existing H∞control method, which is demonstrated via simulations. In addition, a three-dimensional Lorenz chaotic model is employed to certify the applicability of the theoretical findings.

This paper investigates synchronization in complex dynamical networks (CDNs) with interval time-varying delays. The CDNs are representative of systems composed of a large number of interconnected dynamical units, and for the purpose of the mathematical analysis, the leading work is to model them as graphs whose nodes represent the dynamical units. At this time, we take note of the importance of each node in networks. One way, in this paper, is that the closeness-centrality mentioned in the field of social science is grafted onto the CDNs. By constructing a suitable Lyapunov-Krasovskii functional, and utilizing some mathematical techniques, the sufficient and closeness-centrality-based conditions for synchronization stability of the networks are established in terms of linear matrix inequalities. Ultimately, the use of the closeness-centrality can be weighted with regard to not only the interconnection relation among the nodes, which was utilized in the existing works but also more information about nodes. Here, the centrality will be added as the concerned information. Moreover, to avoid the computational burden causing the nonconvex term including the square of the time-varying delay, how to deal with it is applied by estimating it to the convex term including time-varying delay. Finally, two illustrative examples are given to show the advantage of the closeness-centrality in point of the robustness on time-delay.

This paper designs a new leader-following consensus protocol for second-order multiagent systems with time-varying sampling. For the first time in designing a leader-following protocol, the concept of betweenness centrality is adopted to analyze the information flow in the consensus problem for multiagent systems. By construction of a suitable Lyapunov-Krasovskii functional, some criteria for designing consensus protocols of such systems are established in terms of linear matrix inequalities which can be easily solved by various effective optimization algorithms. One numerical example is given to illustrate the validity of the proposed argument.

This paper proposes new stability and stabilization conditions for discrete-time fuzzy systems with time-varying delays. By constructing a suitable Lyapunov-Krasovskii functional and introducing a new summation inequality based on the inequality of Cauchy-Schwartz form, which enhances the feasible region of the stability criterion for discrete-time systems with time-varying delay, a stability criterion for such systems is established. In order to show the effectiveness of the proposed inequality, which provides more tight lower bound of a summation term of quadratic form, a delay-dependent stability criterion for such systems is derived within the framework of linear matrix inequalities, which can be easily solved by various effective optimization algorithms. Going one step forward, the proposed inequality is applied to a stabilization problem in discrete-time fuzzy systems with time-varying delays. The advantages of the proposed stability and stabilization criteria are illustrated via two numerical examples.

In this brief, an extended dissipativity analysis was conducted for a neural network with time-varying delays. The concept of the extended dissipativity can be used to solve for the H∞, L2-L∞, passive, and dissipative performance by adjusting the weighting matrices in a new performance index. In addition, the activation function dividing method is modified by introducing a tuning parameter. Examples are provided to show the effectiveness and less conservatism of the proposed method.

This paper considers the problem of delay-dependent stability criteria for neural networks with time-varying delays. First, by constructing a newly augmented Lyapunov-Krasovskii functional, a less conservative stability criterion is established in terms of linear matrix inequalities. Second, by proposing novel activation function conditions which have not been proposed so far, further improved stability criteria are proposed. Finally, three numerical examples used in the literature are given to show the improvements over the existing criteria and the effectiveness of the proposed idea.