Event-Triggered Local Control for Nonlinear Interconnected Systems Through Particle Swarm Optimization-Based Adaptive Dynamic Programming

This article investigates local control problems for nonlinear interconnected systems by using adaptive dynamic programming (ADP) with particle swarm optimization (PSO). Through constructing a proper local value function, a local critic neural network, whose weight vector is tuned via the PSO algorithm, is employed to solve the local Hamilton–Jacobi–Bellman equation. By introducing the event-triggering mechanism, the sampling time instants of each interconnected subsystem are determined by establishing a proper event-triggering condition. Then, the ADP-based event-triggered local control policy can be derived indirectly to ensure the closed-loop nonlinear interconnected system to be asymptotically stable through the Lyapunov stability analysis. The positive lower bound on the minimal intersampling instant for each interconnected subsystem is provided to exclude the Zeno behavior. Simulation results of a practical system and a numerical example demonstrate the effectiveness of the present event-triggered local control scheme.