Event-Triggered Adaptive Inverse Optimal Control for Stochastic MIMO Nonlinear CPSs under Hybrid Cyber-Attacks

This paper investigates the event-triggered adaptive inverse optimal control problem for a class of stochastic Multiple-input multiple-output (MIMO) nonlinear cyber-physical systems (CPSs) under hybrid cyber-attacks. The hybrid cyber-attack model, consisting of denial-of-service (DoS) and False Data Injection (FDI) attacks, is characterized by a probabilistic Bernoulli process to better capture realistic attack scenarios. Based on the backstepping approach, an event-triggered adaptive optimal control scheme is developed by integrating inverse optimal design with a switching-threshold event-triggered mechanism. The proposed method is applicable to stochastic MIMO systems and ensures that the closed-loop system is semi-globally uniformly ultimately bounded (SGUUB) in probability. Finally, simulation results are presented to demonstrate the effectiveness and feasibility of the proposed strategy.