Observer‐Based Adaptive Sliding Mode Control for Fuzzy Markov Jump Systems Subject to Deception Attacks: A Q‐Learning Event‐Triggered Communication Protocol

Abstract

This article addresses the secure control problem for nonlinear Markov jump systems subject to communication constraints and false data injection attacks. The Takagi–Sugeno (TS) fuzzy model is employed to describe the systems under consideration, and the Q‐learning concept is utilized to enhance the dynamic event‐triggered mechanisms (DETM) and reduce network load without compromising control performance. The main contribution of this work lies in designing an adaptive fuzzy sliding mode observer (SMO) with mismatched premise variables to estimate compromised system states and in synthesizing a sliding mode controller (SMC) to maintain the stochastic stability of the closed‐loop system while guaranteeing the reachability of the sliding‐mode dynamics. Moreover, a feasible solution algorithm based on a Real‐Valued Genetic Algorithm (RVGA) is proposed to synthesize the controller and observer gains and to optimize system performance. Finally, extensive simulations on the Chua chaotic system are conducted to validate the effectiveness of the proposed control strategy.