Data-driven optimal trajectory tracking control of autonomous surface vessels via reinforcement learning

This paper presents an optimal trajectory tracking control algorithm for autonomous surface vessels (ASVs) using data-driven reinforcement learning (RL) to address challenges arising from model uncertainties and time-varying external disturbances in complex marine environments. To ensure robust performance under these conditions, we first employ the H ∞ control method. Then, we design a model-based RL algorithm to achieve the optimal trajectory tracking control law for the ASV despite uncertainties and disturbances. In addition, we extend the model-based RL algorithm to a model-free data-driven RL algorithm, removing the requirement for model of the ASV. The model-free algorithm directly learns the optimal control law from real-time data, providing a more flexible solution when the model of the ASV is unknown. Simulations are conducted to verify the proposed algorithms.