Objectives This study proposes a fixed-time trajectory tracking control strategy to address the challenges of unmodeled dynamics and external disturbances in underactuated unmanned surface vessels (USVs).

Methods First, the tracking error system is divided into two channels for controller design through model transformation. Next, in order to accommodate unknown system dynamics and external disturbances, a minimum-learning-parameter-based neural network is adopted to compensate for uncertainties. After that, a fixed-time sliding mode controller is proposed with the application of a hyperbolic tangent function to ensure the fast convergence of tracking errors.

Results The numerical simulation results show that fixed-time convergence for tracking errors can be guaranteed independent of the initial state.

Conclusions The designed control scheme not only features the effective estimation of uncertainties within the system, but also demonstrates a robust disturbance rejection capability, providing valuable insights for the fixed-time control of USVs.