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In this paper, an adaptive sliding mode control strategy based on nonlinear disturbance observer and radial basis function neural network is presented for trajectory tracking of underactuated unmanned surface vessel (USV) with model parameter uncertainties and unknown external time-varying disturbances. Specifically, a novel trajectory tracking guidance law is proposed based on the three degree of freedom model of USV and line of sight guidance law. Meanwhile, disturbance observers and radial basis function neural network are introduced to estimate the unknown external disturbance and model uncertainties, which effectively improve the robustness of the system. A trajectory tracking controller is designed subsequently by combining adaptive rate and sliding mode control to diminish the influence of actuator fault. It is proved that the control system is stable based on Lyapunov function methods. The simulation results illustrate that the designed control system can achieve precise control of underactuated USV in complex environments. Compared to the comparison methods, the average position tracking error is reduced by more than 80%, which verifies the stability and robustness of the proposed method.