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

First, performance and feasibility constraints are established for the vessel's navigation based on the precision and safety requirements of autonomous ships in shallow waters. Next, to address the issues of the path parameter representation and convergence requirements of the controller, a barrier Lyapunov function (BLF) combined with a fixed-time convergence strategy is applied. A path-dependent controller capable of converging within a fixed time is then designed, and radial basis function neural networks (RBFNN) and adaptive robust terms are used to handle nonlinearities and environmental disturbances. Finally, the intelligent unmanned surface vehicle model "Dazhi" is used to simulate shallow water effects, and the controller's performance is analyzed through simulations.

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.

The proposed control strategy is innovative and significant in addressing path-dependent constraints for ship navigation, ensures precise path tracking within a fixed time, and 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.