Event-Triggered Output Feedback Synchronization of Master-Slave Neural Networks Under Deception Attacks

Fuzzy adaptive event-triggered distributed control for a class of nonlinear multi-agent systems

In this work, we examine an adaptive and event-triggered distributed controller for nonlinear multi-agent systems (MASs). Second, we present a fuzzy adaptive event-triggered distributed control approach using a Lyapunov-based filter and the backstepping recursion technique. Next, the controller and adaptive rule presented guarantee that all tracking errors between the leader and the follower converge in a limited area close to the origin. According to the Lyapunov stability theory, this demonstrates that all other signals inside the closed loop are assured to be semi-globally, uniformly and finally constrained. Finally, simulation tests are conducted to illustrate the effectiveness of the control mechanism.

Synchronization of Switched Neural Networks via Attacked Mode-Dependent Event-Triggered Control and Its Application in Image Encryption

It is challenging to synchronize switched time-delay systems when some modes are uncontrolled and the dwell time (DT) of controlled mode is very small. Therefore, in this article, global exponential synchronization almost surely (GES a.s.) in a cluster of switched neural networks (NNs) with hybrid delays (time-varying delay and infinite-time distributed delay) is investigated, where transition probability (TP)-based random mode-dependent average DT (MDADT) switching is considered. A novel mode-dependent pinning event-triggered controller with nonidentical deception attacks is proposed to save the communication resource and derive less conservative results. The two necessary and restrictive conditions in existing papers that the value of the Lyapunov-Krasovskii functional (LKF) before switching instants should be smaller than that after corresponding instant and the DT of each switching mode is restricted by the sampling intervals of the event trigger are moved. Sufficient conditions in terms of linear matrix inequalities (LMIs) are given to guarantee the GES a.s., even though both synchronizing and nonsynchronizing modes coexist and maybe the minimum DT of synchronizing modes is very small. Numerical examples, including image encryption, are provided to demonstrate the merits of the new technique.

Novel LKF Method on H∞ Synchronization of Switched Time-Delay Systems

This article investigates H_∞ global asymptotic synchronization (GAS) of switched nonlinear systems with delay. By introducing mode-dependent double event-triggering mechanisms (DETMs), the communication resources in both system-controller (S-C) channel and controller-actuator (C-A) channel are saved as much as possible. By designing a new multiple Lyapunov-Krasovskii functional (LKF) with time-varying matrices and developing novel analysis techniques such that the increment of the LKF at switching instant is smaller than one, not only the conservatism of obtained results is greatly reduced but also the nonweighted L₂ -gain is convenient to be derived without using any conservative transformation. The exclusion of the Zeno behavior of the DETMs is proved. Synchronization criteria formulated by linear matrix inequalities (LMIs) are given, by which the control gains, event-triggering weights, as well as the minimum L₂ -gain are simultaneously designed. Numerical examples demonstrate the low conservatism of the theoretical analysis. Meanwhile, image processing on the basis of the H_∞ GAS is provided to further illustrate the perfect performance.

Bounded synchronization for uncertain master-slave neural networks: An adaptive impulsive control approach

This paper investigates the bounded synchronization of the discrete-time master-slave neural networks (MSNNs) with uncertainty. To deal with the unknown parameter in the MSNNs, a parameter adaptive law combined with the impulsive mechanism is proposed to improve the estimation efficiency. Meanwhile, the impulsive method also is applied to the controller design for saving the energy. In addition, a novel time-varying Lyapunov functional candidate is employed to depict the impulsive dynamical characteristic of the MSNNs, wherein a convex function related to the impulsive interval is used to obtain a sufficient condition for bounded synchronization of the MSNNs. Based on the above condition, the controller gain is calculated utilizing an unitary matrix. An algorithm is proposed to reduce the boundary of the synchronization error by optimizing its parameters. Finally, a numerical example is provided to illustrate the correctness and the superiority of the developed results.

Exponential synchronization of coupled neural networks under stochastic deception attacks

In this paper, the issue of synchronization is investigated for coupled neural networks subject to stochastic deception attacks. Firstly, a general differential inequality with delayed impulses is given. Then, the established differential inequality is further extended to the case of delayed stochastic impulses, in which both the impulsive instants and impulsive intensity are stochastic. Secondly, by modeling the stochastic discrete-time deception attacks as stochastic impulses, synchronization criteria of the coupled neural networks under the corresponding attacks are given. Finally, two numerical examples are provided to demonstrate the correctness of the theoretical results.