Dynamic Event-Triggered Fuzzy Optimal Consensus Control for Stochastic Multiagent Systems Under Switching Topology

This article investigates the problem of data-based distributed fuzzy adaptive optimal consensus control for a class of nonlinear stochastic multiagent systems (MASs) under switching topology. In control design, the stochastic Hamilton–Jacobi–Bellman equation with unknown system dynamics is learned using the integral reinforcement learning (IRL) algorithm. Combining IRL algorithm and critic fuzzy logic systems, a distributed adaptive optimal consensus control strategy is designed based on dynamic event-triggered mechanism. By employing the topology-dependent Lyapunov function and the average dwell-time method in the stability analysis, it is proved that all signals in the closed-loop system are uniformly ultimately bounded in probability, Zeno behavior can be avoided and the closed-loop system is in Nash equilibrium. Finally, a simulation example is given to illustrate the effectiveness of the developed optimal control approach.