Containment control of networked autonomous underwater vehicles guided by multiple leaders using predictor-based neural DSC approach

This paper considers the containment control of multiple autonomous underwater vehicles (AUVs) in the presence of model uncertainty and time-varying ocean disturbances. A new predictor-based neural dynamic surface control design approach is proposed to develop adaptive containment controllers, under which the trajectories of AUVs converge to the dynamic convex hull spanned by the dynamic leaders. The prediction errors are used to update the neural adaptive laws, which enables fast identifying the vehicle dynamics without excessive knowledge of their dynamical models. The stability properties of the closed-loop network are established via Lyapunov analysis, and the containment errors converge to an adjustable neighborhood of the origin. Comparative studies are given to show the effectiveness of the proposed method.