BLF-based Adaptive Path Following Control for Unmanned Surface Vehicles under Shallow Water Effects

ObjectivesThis study investigates how to effectively address path-dependent constraints during path tracking of autonomous surface vessels in complex waterways, ensuring navigation safety and stability. Methods A fixed-time adaptive control strategy based on the Barrier Lyapunov Function (BLF) is proposed, combined with adaptive backstepping to design the controller. This approach avoids the "complexity explosion" issue common in traditional controller design and enhances the control system's capability to handle path-dependent constraints. Simulation validation is performed using the "Dazhi" intelligent vessel model under shallow water effects to analyze the performance of the controller. Results Simulation results show that the path tracking error rapidly converges to the predetermined region without violating the constraint requirements, verifying the effectiveness of the controller. Conclusions The proposed control strategy not only addresses path-dependent constraints but also ensures precise path tracking within a fixed time, offering both theoretical and practical value.