USV Path-Following Control Based On Deep Reinforcement Learning and Adaptive Control

This paper presents a guidance and control scheme for an unmanned surface vehicle. The approach combines a deep reinforcement learning based guidance law that can learn the dynamics of vessel with an adaptive sliding mode controller to achieve path-following. The guidance implements a deep deterministic policy gradient algorithm to obtain the desired heading command, whereas the adaptive control drives the heading and surge speed. The proposed guidance has self-learning ability based on evaluative feedback, which does not require any prior knowledge of the dynamic system, and the controller exhibits robustness against bounded uncertainties and perturbations, control gain non-overestimation, and chattering reduction. Simulation results show that the proposed guidance and control law achieves fast convergence and small overshoot, and improved performance when compared against line-of-sight based guidance laws.