[Objective]This paper presents a tracking control method based on dynamic programming guid-ance to address the challenges presented by the autonomous recovery of underactuated unmanned surface vehicles(USVs).[Methods]At the kinematic level,constant bearing approach(CB)guidance is combined with a dynamic window algorithm(DWA)to guide the USV in achieving target tracking and dynamic obstacle avoidance.At the dynamic level,considering the uncertainties in the model parameters and recovery environ-ment,a radial basis function neural network(RBFNN)is employed to design a dynamic sliding mode control-ler for the tracking control of the guidance output.Finally,the stability of the system is analyzed using Lya-punov theory.[Results]The simulation results demonstrate that the proposed method enables the USV to exhibit stable tracking performance,effectively avoid dynamic obstacles during the recovery process and ad-apt to uncertain factors in the estimation model and unknown environmental disturbances.[Conclusion]The proposed method exhibits strong robustness and flexibility,providing valuable references for the guidance and target tracking of USVs during recovery in dynamic environments.
维普
万方数据