Adaptive Event-Triggered Compensation Control for Nonlinear Semi-Markov Jump Systems With False Data Injection Attacks

Abstract

This article investigates the problem of asynchronous attack-compensated control for discrete-time nonlinear semi-Markov jump systems (S-MJSs) subject to false data injection (FDI) attacks. To mitigate the adverse effects of malicious data, a resilient controller is constructed by combining a false-signal observer with a compensation-based control law. Concurrently, considering limited network resources, a memory-based adaptive event-triggered scheme is proposed to enhance control performance while significantly reducing communication overhead. Recognizing the practical constraints in transition information identification, the semi-Markov kernel (SMK) and the high-level homogeneous Markov chain are assumed to be partially available. By employing a mode-rule-dependent Lyapunov function together with the linear matrix inequality method, sufficient conditions are derived to guarantee the $H_{\infty }$ performance of the control systems. Finally, a single-link robot arm model is employed to validate the efficacy of the proposed compensation control strategy.