Adaptive Containment Control for Uncertain Networked Agent Systems with Second-Order Dynamics

This article proposes a neural network-based adaptive distributed containment controller for second-order integral multi-agent systems with nonlinear dynamics and external disturbances. Firstly, the proposed controller effectively suppresses the effects of nonlinear dynamics and external disturbances while achieving containment control. Secondly, for the adaptive update law of neural networks, unlike existing studies, we simultaneously consider the influence of position error and velocity error on the convergence speed of the weight matrix. Further, two positive definite matrices are constructed to prove the stability of the system based on Lyapunov theory, and the upper bound for the containment errors is provided. Finally, the effectiveness of the proposed method is verified through numerical simulation experiments.