Dynamic Event-Triggered Adaptive Neural Network Decentralized Output-Feedback Control for Nonlinear Interconnected Systems With Hybrid Cyber Attacks and Its Application
Yahui Cui, Haibin Sun, Linlin Hou, Kaibo Shi
Abstract
In this article, a dynamic event-triggered adaptive neural network decentralized nonrecursive output-feedback control scheme for nonlinear interconnected systems under hybrid cyber attacks is first proposed, where the hybrid cyber attacks, containing deception attacks and denial-of-service (DoS) attacks, obey Bernoulli distribution. Taking into account the restrictions imposed by network transmission, a dynamic event-triggered mechanism is introduced to fulfill the analysis and design task. A decentralized linear observer is established to estimate the unknown states. Meanwhile, a decentralized adaptive output-feedback controller is developed in a nonrecursive method with the help of neural network technology. The proposed control scheme can ensure that all signals in the closed-loop system are bounded. Furthermore, Zeno phenomenon can be effectively avoided. Finally, simulation results validate the feasibility of the proposed control scheme.