Deep Reinforcement Learning for Variable Tension Control of Unmanned Underwater Vehicle Arresting Gear Under Nonlinear Effects

Large Unmanned Underwater Vehicles (UUVs) are playing an increasingly critical role in complex marine missions due to their enhanced payload and endurance capabilities. However, the safe recovery of these platforms remains a significant challenge, complicated by their high inertia, strong hydrodynamic interactions, and unpredictable environmental disturbances. In particular, the nonlinear coupling effects between the mechanical structure and the hydrodynamic environment exert a considerable influence on the system, accounting for nearly 50% of the tension on the arresting cable. To address these challenges, this paper proposes a variable tension control strategy for a UUV underwater arresting recovery system, utilizing a Well-Shaped Reward Entropy-regularized Proximal Policy Optimization (WSR-E-PPO) algorithm. In this framework, the real-time velocity and displacement of the UUV are utilized to represent the spatiotemporal characteristics of the recovery state, and a hybrid reward function integrating sparse and continuous rewards based on Potential-Based Reward Shaping (PBRS) is designed. Simulation results demonstrate that the proposed method enables the UUV to return to the docking point without oscillation, while effectively limiting the total recovery time to approximately 80 s—a 37.5% reduction compared with existing methods. Furthermore, the strategy ensures smoother tension regulation throughout the process. These findings provide a solid technical foundation and assurance for the stable and safe underwater recovery of large UUVs.