NN-Based Dynamic Event-Triggered Consensus Tracking for Nonlinear Multi-Agent Systems via a Single Parameter Learning Method

This paper investigates adaptive dynamic event-triggered neural network (NN) consensus tracking for nonlinear multi-agent systems (MASs). A distributed adaptive controller is designed for MASs with unknown nonlinear functions and external disturbances. A single parameter learning method is proposed to simplify the controller design. Moreover, the dynamic event-triggered mechanism is designed to conserve communication resource. The designed control law ensures that all signals of the MASs are uniformly bounded, and the tracking errors of all agents converge to a compact set while avoiding Zeno behavior. An expository simulation example reveals the virtue of the presented method.