基于数据-物理双驱动的强化学习无人船航迹跟踪控制OA

A data-physics dual-driven(DPD)reinforcement learning(RL)-based optimal control method considering input constraints and external environmental disturbances was proposed for unmanned surface vessels(USVs)applied in intelligent marine ranching.Firstly,to address the challenges of modeling USVs'unknown dynamics and insufficient interpretability of traditional data-driven methods,a DPD model was constructed by integrating the physical laws of ship dynamics with the data learning capability of recurrent neural networks(RNN),enabling accurate reconstruction of unknown complex dynamic characteristics and enhancing the model's physical interpretability.Secondly,a control framework was designed via the combination of backstepping and optimal control methods.An RL-based optimal control strategy was proposed to improve the USVs'continuous interaction capability with complex marine environments,achieving a dynamic balance between tracking accuracy and control resource consumption.Meanwhile,the physical constraints imposed by the hull's load-bearing capacity on control inputs were incorporated into the controller design.Thirdly,the uniform ultimate boundedness(UUB)of all signals in the closed-loop system was proved by using Lyapunov stability theory.Finally,comparative simulation results under Level 2 sea conditions demonstrated that the proposed method improved tracking accuracy by 42.6%compared with the physics-principle-driven(PPD)method,and reduced control input cost by 26.9%relative to the PPD method and 9.6%compared with traditional backstepping control,which fully verified the effectiveness and superiority of the proposed method.