Adaptive cooperative formation control of autonomous surface vessels with uncertain dynamics and external disturbances

This paper investigates the leader-follower cooperative formation control problem of autonomous surface vessels (ASVs) with uncertain dynamics and external disturbances. Especially, ASVs can communicate with each other under a directed interaction topology. Based on directed graph theories, backstepping and the minimal learning parameter (MLP) algorithm, a novel distributed robust formation controller with two different adaptive laws is developed for each ASV. Dynamic surface control (DSC) is utilized to eliminate repeated derivative of virtual control laws, which is important to generate real-time control signals. Neural networks (NNs) approximation combined with an MLP-based adaptive law is incorporated into the proposed controller to enhance the robustness against model uncertainties. Then only one learning parameter instead of the enormous weights matrix is estimated for each ASV. An auxiliary adaptive law is designed to obtain a continuous controller when compensating approximation errors and disturbances. It is shown that desired formation shapes can be achieved with the proposed controller if the interaction topology has a directed spanning tree, and formation errors are guaranteed to be semi-global uniformly ultimately bounded (SGUUB). Simulations and comparison results are provided to illustrate the effectiveness of theoretical results.