A Predictor-based Neural DSC Design Approach to Distributed Coordinated Control of Multiple Autonomous Underwater Vehicles

This paper considers the distributed coordinated control problem of multiple autonomous underwater vehicles with a time-varying reference trajectory. Each vehicle is subject to model uncertainty and time-varying ocean disturbances. A new predictor-based neural dynamic surface control design approach is proposed to develop distributed adaptive node controllers, under which synchronization between vehicles can be reached under the condition that the augmented graph induced by the vehicles and the reference trajectory contains a spanning tree. 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. Simulation results demonstrate the performance improvement of the proposed control strategy.