This paper presents a tracking control method based on dynamic programming guidance to address the challenges presented by the autonomous recovery of underactuated unmanned surface vehicles (USVs).

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 environment, a radial basis function neural network (RBFNN) is employed to design a dynamic sliding mode controller for the tracking control of the guidance output. Finally, the stability of the system is analyzed using Lyapunov theory.

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 adapt to uncertain factors in the estimation model and unknown environmental disturbances.

The proposed method exhibits strong robustness and flexibility, providing valuable references for the guidance and target tracking of USVs during recovery in dynamic environments.