Objectives This study investigates how to effectively address path-dependent constraints during path following of unmanned surface vessels in complex waterways, ensuring navigation safety and stability.

Methods Firstly, performance and feasibility constraints were established for the vessel’s navigation based on the precise and safety requirements of autonomous ships in shallow waters. Then, to address the issues of path parameter representation and convergence requirements of the controller, a barrier Lyapunov function (BLF) combined with a fixed-time convergence strategy was applied. A path-dependent controller capable of converging within a fixed time was designed, and radial basis function neural networks (RBFNN) along with adaptive robust terms were used to handle nonlinearities and environmental disturbances. Finally, the intelligent ship model “Dazhi” was used to simulate the shallow water effects, and the controller’s performance was analyzed through simulations.

Results The simulation results show that the path tracking error converges rapidly to the desired region without violating the constraints. Compared to the unconstrained case, the controller demonstrates clear advantages in convergence speed and precision, verifying its effectiveness and robustness.

Conclusions The proposed control strategy is innovative and significant in addressing path-dependent constraints for ship navigation, ensuring precise path tracking within a fixed time. It has significant theoretical and practical application value. Future research may further optimize the control strategy to address more complex water environments and higher-precision path tracking tasks.