Optimal trajectory tracking control for USVs under dynamic uncertainties and time-varying disturbances via PI and IRL algorithms

Abstract

This paper presents a model-free optimal control framework for trajectory tracking of Unmanned Surface Vehicles operating under unknown dynamics and time-varying disturbances via Policy Iteration (PI) and Integral Reinforcement Learning (IRL) algorithms. The IRL-PI controller is developed based on an order reduction technique and an off-policy Actor-Critic neural network structure, allowing real-time approximation of the Hamilton-Jacobi-Bellman solution without requiring model knowledge. Simulation results on a three three-degree-of-freedom (3-DOF) USV model demonstrate that the proposed method outperforms conventional controllers in both tracking accuracy and robustness. These results highlight the potential of the IRL-PI controller to develop robust control solutions for complex marine systems operating in uncertain and dynamic environments.