A Particle Swarm Optimization Event-Triggered Approach to Adaptive Sliding Mode Control of Markov Jump Networked Systems

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. Note to Practitioners—This paper was motivated by the effective control problem in systems such as robotic manipulators and microgrids, which may exhibit stochastic behaviors over a network. As the signal data is transmitted through the network, an important issue arises: how to conserve network bandwidth to prevent congestion when the bandwidth is limited, and how to save system resources when the system reaches a steady state, rendering further updates of control signals unnecessary. Recent studies have proposed static or dynamic event-triggered approaches, which have improved efficiency and system resource usage to some extent. This paper introduces an innovative particle swarm optimization-based adaptive sliding mode approach, which not only enhances the efficiency of existing event-triggered methods but also ensures the robustness of the plant in the presence of unknown matched nonlinear disturbances. The advantages of the proposed approach are verified through a simulation study conducted on a circuit.