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• 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.

This paper studies the sliding mode control method for coupled delayed fractional reaction-diffusion Cohen-Grossberg neural networks on a directed non-strongly connected topology. A novel fractional integral sliding mode surface and the corresponding control law are designed to realize global Mittag-Leffler synchronization. The sufficient conditions for synchronization and reachability of the sliding mode surface are derived via the hierarchical method and the Lyapunov method. Finally, simulations are provided to verify our theoretical findings.

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.

In this paper, the synchronization problem of inertial neural networks with time-varying delays and generally Markovian jumping is investigated. The second order differential equations are transformed into the first-order differential equations by utilizing the variable transformation method. The Markovian process in the systems is uncertain or partially known due to the delay of data transmission channel or the loss of data information, which is more general and practicable to consider generally Markovian jumping inertial neural networks. The synchronization criteria can be obtained by using the delay-dependent Lyapunov-Krasovskii functionals and higher order polynomial based relaxed inequality (HOPRII). In addition, the desired controllers are obtained by solving a set of linear matrix inequalities. Finally, the numerical examples are provided to demonstrate the effectiveness of the theoretical results., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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.)