Robust Optimal Consensus Control for Nonlinear Multi-agent Systems: Two Hybrid Dynamic Event-Triggered-Based Approach

Abstract

In this paper, the dynamic event-triggered robust optimal consensus control problem is investigated for nonlinear multi-agent systems (MASs) with matched and mismatched disturbances via integral sliding mode (ISM) control and the adaptive dynamic programming (ADP) algorithm. First, a dynamic event-triggered fixed-time ISM controller is designed to guarantee that the system state converges to the predefined ISM manifold, thereby eliminating the matched disturbances. Next, the consensus control problem is transformed into an optimal control problem by constructing neighborhood error dynamics and a new modified cost function; thus, an event-triggered-based coupled Hamilton-Jacobi-Bellman equation (HJBE) is established. Then, an ADP-based single-critic neural network (NN) is constructed to solve coupled HJBE to obtain the event-triggered optimal controller, in which the NN weight is updated only at the triggering instants. Through the implementation of these two dynamic event-triggered mechanisms, resources and controller execution time can be saved. It is proved that the whole closed-loop system signals are uniformly ultimately bounded by the Lyapunov technique. Finally, two illustrative examples verify the effectiveness and superiority of the proposed control scheme.