Event-based Neuro-Adaptive Containment Control for Uncertain Networked Agent Systems With External Disturbances

In this work, the robust containment control problem of uncertain networked agent systems is addressed. The dynamics of both the followers and the leaders involve uncertain nonlinear components. Theoretically, a neural network-based adaptive control scheme with the event-triggered mechanism is designed for the follower agents to approach the convex hull which is composed of the leader agents. Furthermore, a unified event-triggered mechanism is designed to ensure that the neural networks synchronize with the reduced update frequency of the robust controller, while ensuring that both are updated solely when the system error state reaches a predetermined triggering condition. The designed event-triggered containment control scheme minimizes unnecessary communication and computation resulting from periodic updates, thereby enhancing the efficiency of communication and computational resource utilization. Then, by utilizing the Lyapunov stability theorem, it is proven that all signals of the closed-loop system are globally uniformly ultimately bounded, and Zeno behavior is strictly excluded. Finally, the effectiveness of the proposed containment control scheme is verified through numerical simulation results.