Robust adaptive event-triggered path following control for autonomous surface vehicles in shallow waters

Abstract

This paper utilizes an adaptive event-triggered mechanism to investigate the path following control of autonomous surface vehicles (ASVs) in shallow waters. Given the challenges posed by the shallow water environment to ASV maneuverability and heading stability, the controller design must enhance the robustness and reduce model complexity. Firstly, a time-varying drift angle-based course guidance law was proposed using a position error dynamics model and a reduced-order extended state observer. Secondly, to simplify the controller's design steps, the ASV's surge and sway motion models are decoupled and transformed. Then, an event-triggered mechanism controller was designed to activate the ASV's rudder angle and shaft speed. In addition, a new robust term was introduced to further estimate the residuals of the RBF neural network, the external disturbances and the event-triggered residual term, thus eliminating the requirement for an upper bound on the unknown disturbances and residuals. The proposed control method based on the robust adaptive event-triggered mechanism can guarantee that all estimation and tracking errors are uniformly ultimately bounded. Simulation results demonstrate that the controller can ensure effective path following of vessels in shallow waters.