A Particle Swarm Optimization Event-Triggered Approach to Adaptive Sliding Mode Control of Markov Jump Networked Systems
Haocheng Lou, Baoping Jiang, Zhen Liu
Abstract
This work focuses on adaptive sliding mode control for networked Markov jump systems using a dynamic event-triggered strategy. Unlike previous studies, this mechanism employs a particle swarm optimization algorithm to compute the optimal triggering threshold based on the system’s continuous output values. First, this study designs an event-triggered state observer based on the system conditions, and the error dynamics can be obtained according to the definition of the error. Second, a hyperbolic-type cost function is designed for the event-triggered mechanism, and a dynamic event-triggered scheme is constructed using the particle swarm optimization algorithm. Third, an integral sliding surface is established to obtain the sliding mode dynamics. Following this, a sliding mode controller incorporating adaptive laws is designed, and the reachability is formally demonstrated, and the closed-loop system’s stochastic stability is examined through stochastic Lyapunov function method. Finally, the effectiveness and superiority of the proposed method are verified through RLC circuit.