A GA-based dynamic event-triggered adaptive sliding mode control of networked Markov jump systems

This article addresses the issue of adaptive sliding mode control for networked Markov jump systems, and introduces a control strategy that leverages a dynamic event-triggered mechanism. Unlike previous studies, this mechanism presented here is grounded in genetic algorithms and is sensitive to the system’s state dynamics, thereby offering enhanced adaptability to diverse system variations. Firstly, a state observer is designed to establish the error dynamics. Then, an integral sliding mode surface is developed, which facilitates the derivation of the sliding mode dynamics. Secondly, an objective function related to the stability of the system is designed, and a dynamic event-triggered mechanism is established using genetic algorithms. Thirdly, the stochastic Lyapunov function approach is employed to analyze the stability of the closed-loop system. Fourthly, an adaptive sliding mode controller is designed to ensure finite-time reachability. Finally, the proposed methodology is confirmed to be effective and superior through both numerical simulations and practical applications.