Cooperative Target Fencing of Uncertain Multi-USVs: A Reinforcement Learning-Based Approach
针对受外部干扰和动力学不确定的多无人艇系统，提出一种两阶段控制策略，先用滑模控制补偿干扰，再用软演员-评论家强化学习处理不确定性和避碰，仿真验证了效率优势。
This article is dedicated to solving the cooperative collision-free target fencing control problem for a class of multiple unmanned surface vehicle (multi-USV) systems subject to external disturbances and uncertain dynamics. While existing control strategies often struggle to simultaneously address external disturbances, internal system uncertainties, and safety constraints in real-world applications, this study proposes a robust two-stage control strategy to effectively tackle these challenges. In the first stage, a baseline controller leveraging sliding mode control (SMC) is developed to effectively compensate for external disturbances and establish fundamental target fencing capabilities. Building on this foundation, the second stage introduces an advanced controller enhanced by soft actor–critic (SAC) reinforcement learning (RL) technique, which is specifically tailored to address internal system uncertainties and guarantee collision avoidance. Finally, numerical simulations are performed to validate the proposed approach, demonstrating its advantages over conventional RL-based methods in terms of efficiency.