Neural-Network-Based Security Control for T-S Fuzzy System With Cooperative Event-Triggered Mechanism

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 and observer-to-controller 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 technology. Furthermore, using the Lyapunov function, sufficient conditions for ensuring that T-S FMJSs are mean square exponential ultimate bounded are derived. Finally, the effectiveness of our proposed approach is demonstrated through a simulation example.