Neural-Network-Based Security Control for T-S Fuzzy System With Cooperative Event-Triggered Mechanism
Cheng Tan, Chengzhen Gao, Jinzhu Peng, Xiangpeng Xie, Yaonan Wang
Abstract
In this article, we investigate the problem of security control for T-S fuzzy Markov jump systems (FMJSs) under actuator faults and deception attacks, while introducing a cooperative event-triggered mechanism (CETM). In order to enhance the efficiency of communication resources, we develop the CETM in the forward channel, which operates concurrently on sensor-to-observer (STO) and observer-to-controller (OTC) channels utilizing a united event generator. Additionally, we design an attack-compensating controller to eliminate the impact of nonlinear malicious injection information generated by deceptive attacks on the system, where the compensation signal is generated by approximating the attack signal using radial basis function neural network (RBFNN) technology. Furthermore, using the Lyapunov function, sufficient conditions for ensuring that T-S FMJSs are mean square exponential ultimate bounded (MSEUB) are derived. Finally, the effectiveness of our proposed approach is demonstrated through a simulation example.