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This paper studies the secure synchronization problem for two-time-scale complex dynamical networks with unknown dynamics information and malicious attacks. The challenge is that under complex dynamic networks with unknown dynamic information, all system matrices are unknown. To ameliorate this conundrum, we design a reinforcement learning algorithm, conjoined with a full-order processing of singularly perturbed parameters, with the express objective of securing the stability of the synchronization error system within a context devoid of adversarial intrusions. Second, in order to reduce the impact of malicious attacks, a switching function is developed based on feedback gain. Besides, it is proved that the proposed distributed controllers can still guarantee the convergence of the synchronization error system under malicious attacks. Finally, two numerical examples are given to illustrate the applicability and effectiveness of the proposed algorithm. • A distributed model-free sliding mode controller is presented for SPCDNs. • An iterative algorithm based on RL is employed. • The controller can weaken the influence of the attacks and achieve secure synchronization. [ABSTRACT FROM AUTHOR]

• A novel Lyapunov–Krasovskii functional with a looped-functional is constructed % t k + 1 . • A more general inequality is utilized to process the integral with a derivative of the state variable. • An % H ∞ performance index is raised to deal with the control problem for sMJCDNs. This paper investigates the H ∞ exponential synchronization problem of delayed semi-Markov jump complex dynamical networks by utilizing the sampled-data control scheme. Through introducing a semi-Markov jump model, where the transition rate is related to the sojourn time, the addressed issue is therefore more general. By constructing a suitable Lyapunov–Krasovskii functional with a looped-functional and adopting an advanced inequality method, sufficient conditions for the H ∞ exponential synchronization of delayed semi-Markov jump complex dynamical networks are presented. A numerical example is finally employed to confirm the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies the existence and stability of periodic solution of the high-order discrete-time Cohen–Grossberg neural networks with varying delays. The properties of M-matrix and the contracting mapping principle are used to obtain a sufficient condition that guarantees the uniqueness and global exponential stability of the periodic solution. In addition, a numerical example is given that demonstrates the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

• A modified design process of finite-time control (FTC) is achieved by utilizing prescribed performance control (PPC) and pre-assigned finitetime function (PFTF). • The states can converge to a predefined region within the pre-assigned finite-time. • The proposed method is more flexible and simpler since the convergence time is not related with the design parameters and initial values. • Nussubaum gain is employed to tackle the FTC problem for parameter systems with unknown signs. This paper mainly studies an adaptive finite-time control problem for a kind of uncertain strict feedback systems with unknown control gains. First, the original strict-feedback system is transformed into a pure-feedback system. And then, we propose a modified adaptive finite-time design idea on the basis of the property of pre-assigned finite-time function (PFTF) and Nussbaum gain. The designed controller can make sure that all states converge to an arbitrarily region in finite-time and signals in the closed-loop system are finite-time bounded. Moreover, the presented controller is smooth and the settling time does not rely on the initial values and design parameters. Finally, the simulation study is made to illustrate the innovativeness for the proposed scheme. [ABSTRACT FROM AUTHOR]

• The PDTSM, for the first attempt, is considered to describe the variation of the TPs for discrete-time slow sampling MJSPSs. • The impact of uncertain measurement on system performance is studied. • Some sufficient conditions ensuring the FES is MSES under the double-layer switching mechanism, are given. This article concentrates on the extended dissipative filtering for slow sampling singularly perturbed systems based on the double-layer switching mechanism containing the Markov jump signal and the persistent dwell-time switching signal simultaneously. The Markov jumping signal, as the upper layer switching signal, is utilized to describe the jumping of system modes. Then, the persistent dwell-time switching signal is introduced as a lower layer switching signal to characterize the variation for transition probabilities of the Markov chain. Besides, the uncertainties of actual measurement output are assumed to randomly occur, which are described by a set of Bernoulli distributed white sequences. Furthermore, some sufficient conditions ensuring the filtering error system being mean-square exponentially stable with an extended dissipative performance are obtained through the Lyapunov stability theory and some advanced linear matrix transformation techniques. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• As the first attempt, we considered the state information required by the sliding mode controller will be randomly dropouts, which is reflected in the obtained equivalent sliding mode controller. • A novel method is utilized for the SMC problem of the PDT switched systems with data dropouts, and a novel SMC law is proposed. • For the PDT switched systems, some sufficient conditions to guarantee the mean-square exponential stability with data dropouts are derived for the PDT switched systems and the H ∞ performance of the system is analyzed through utilizing the linear matrix inequality technique. This paper concentrates on the sliding mode control problem for a set of discrete-time switched systems. As the first attempt, the persistent dwell-time switching rule is considered in the research of sliding mode control for switched systems. Furthermore, data dropouts are frequently encountered in the process of data transmission, and it should be considered in the process of designing the sliding mode control law so that the state trajectories are guaranteed to move in a region around the specified sliding surface. Based on the Lyapunov theory and the matrix convex optimization technique, the main concern of this paper is to construct a sliding mode controller to ensure that the closed-loop system is mean-square exponentially stable with a prescribed weighted H ∞ performance. Finally, the effectiveness and rationality of the obtained results are verified by a numerical example. [ABSTRACT FROM AUTHOR]

• Compared with the previous works about MJSPSs that the controller was assumed to directly obtain the system mode, we utilize the HMM to describe this asynchronous phenomenon, thus the designed controller is more general and appropriate. • Aiming at the Markov jump and singularly perturbed parameter (SPP) in the system, a proper mode-independent sliding surface and an asynchronous SMC law in consideration of the singularly perturbed parameter matrix are proposed based on the HMM approach. • As the first attempt, the fault-tolerant control scheme is applied in the asynchronous SMC for MJSPSs. Combined with it, the mean-square stability with an acceptable H ∞ system performance is achieved. This work studies the asynchronous fault-tolerant sliding mode control for uncertain stochastic jumping systems subject to singular perturbations, in which the controller is assumed to be able to estimate the hidden system mode through a detector with a conditional probability via the hidden-Markov model. Additionally, in consideration of avoiding the system performance decreases generated by the actuator degradation, the synthesized fault-tolerant sliding mode control scheme is first employed. Through applying a common singularly perturbed parameter based sliding surface, this paper attempts to construct a proper asynchronous sliding mode control law that can ensure not only the reachability but also the closed-loop system is stable with an expected H ∞ performance index. By virtue of the mode-dependent Lyapunov function and the hidden-Markov model approach, sufficient conditions are acquired. Besides, solving linear matrix inequalities for obtaining the control gain matrices are given through the convex optimization theory. Eventually, the effectiveness and feasibility of the proposed asynchronous fault-tolerant sliding mode control scheme are verified by a numerical example. [ABSTRACT FROM AUTHOR]

• The mean square exponential stability of high-order Markovian jump reaction-diffusion. • HNNs (RHNNs) with GUTRs is firstly discussed. • The uncertainties exist in not only parameters but also transition rates. • Each transition rate can be completely unknown or only its estimate value is known. • Our models are more comprehensive and some existing results are special cases of ours. • A numerical example illustrates the validity of our findings. This paper focuses on mean square exponential stability of high-order Markovian jump reaction-diffusion HNNs (RHNNs) with uncertain transition rates (GUTRs) and time-varying delays by Lyapunov-Krasovskii functional method and linear matrix inequality (LMI). In this GUTR model, only part of the transition rates can be known, namely, its estimate error and estimate value are known, but the others have no useful information. Our models are more comprehensive and some existing results are special cases of ours. Finally, a numerical example illustrates the validity of our findings. [ABSTRACT FROM AUTHOR]

This article investigates the practical fixed-time synchronization of uncertain coupled neural networks via dual-channel event-triggered control. Contrary to some previous studies, the bipartite synchronization of signed graphs representing cooperative and antagonistic interactions is studied. The communication channel is introduced into deception attacks, which are described by Bernoulli's stochastic variables. Based on the concept of two channels, event-triggered mechanisms are designed for sensor-to-controller and controller-to-actuator channels to reduce communication consumption and controller update consumption as much as possible. Lyapunov and comparison theories are used to derive synchronization criteria and explicit expression of settling time. An example of Chua's circuit system is presented to demonstrate the feasibility of the obtained theoretical results.

In this article, we propose a new concept called average impulsive delay-gain (AIDG) for studying the synchronization of coupled neural networks (CNNs). Based on the viewpoints of impulsive control and impulsive perturbation, we establish some globally exponential synchronization criteria for CNNs. Our methods are well-suited for addressing the synchronization problems of systems subject to hybrid delayed impulses with time-varying impulsive delay and gain. Moreover, we prove that the AIDG has both positive and negative effects on synchronization. Compared to existing research, our conclusions are more applicable and less conservative as the considered hybrid delayed impulses involve more flexible cases. Finally, we validate the effectiveness of our proposed results by applying them to small-world and scale-free network models.

This article investigates the finite-time and fixed-time synchronization for memristive neural networks (MNNs) with mixed time-varying delays under the adaptive aperiodically intermittent adjustment strategy. Different from previous works, this article first employs the aperiodically intermittent adjustment feedback control and adaptive control to drive the MNNs to achieve synchronization in finite time and fixed time. First of all, according to the theories of set-valued mappings and differential inclusions, the error MNNs is derived, and its finite-time and fixed-time stability problems are discussed by applying the Lyapunov function method and some LMI techniques. Moreover, by meticulously designing an effective aperiodically intermittent adjustment with adaptive updating law, sufficient conditions that guarantee the finite-time and fixed-time synchronization of the drive-response MNNs are obtained, and the settling time is explicitly estimated. Finally, three numerical examples are provided to illustrate the validity of the obtained theoretical results.

For networked control systems, it is known that various communication parameters in the channel will pose some fundamental limitations on output tracking control (OTC) performance. In this study, we mainly discuss the limitations resulting from model uncertainties, involving channel and plant. Through using the bivariate stochastic process to model packet loss, and the assumption that channel noise is additive white Gaussian noise (AWGN), two explicit expressions of output tracking performance limitations are derived with the single-degree-of-freedom (SDOF) and two-degree-of-freedom (TDOF) control structure, which shows that the performance of OTC is closely related to the inherent characteristics of the plant, as well as the packet loss rate and power spectral density (PSD) of AWGN. Finally, by considering an illustrative example, the simulation results are verified and analyzed to ensure the effectiveness of treatment methods and results.

An H ∞ congestion tracking control problem for nonlinear transmission control protocol/active queue management (TCP/AQM) network is proposed in this paper. Inspired by the existing network models, a modified TCP/AQM network model with external disturbance and modelling uncertainty is first introduced. And then, a novel function for the desired queue length is defined for the first time, which can simulate the changes in the queue length during a day. Next, a novel AQM scheme is presented to control network congestion by combining H ∞ theory and integral backstepping technique. The proposed method guarantees that the better tracking performance of the queue is achieved, and all the signals are asymptotically stable in the closed-loop system in probability. Finally, the effectiveness of the presented algorithm is tested by simulation results., (Copyright © 2019 ISA. Published by Elsevier Ltd. All rights reserved.)