Model-Free Antidisturbance Autopilot Design for Autonomous Surface Vehicles With Hardware-in-the-Loop Experiments

This article investigates the yaw angle tracking control of an autonomous surface vehicle (ASV) subject to fully unknown internal dynamic, external disturbance, and unknown control input gain. A model-free adaptive antidisturbance autopilot control method is proposed for an ASV without using any model parameters. Specifically, by utilizing real-time and historical data, a data-driven concurrent learning extended state observer (CLESO) method is designed to estimate the unknown ASV model parameters and ensure the convergence of the estimation without requiring persistent excitation. Then, a model-free yaw angle tracking controller is designed based on the data-driven CLESO method. Through Lyapunov stability analysis, the closed-loop system is proven to be input-to-state stable. Simulation and experimental results validate the effectiveness of the proposed CLESO method for the yaw angle tracking of an ASV with fully unknown dynamic model.