Model-Free Antidisturbance Autopilot Design for Autonomous Surface Vehicles With Hardware-in-the-Loop Experiments
Zhouhua Peng, Kai Cui, Huijuan Li, Nan Gu, Lu Liu, Dan Wang
Abstract
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.