基于BLF的浅水效应下无人船自适应路径跟踪控制

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

  • 摘要: 研究无人船在浅水域进行路径跟踪时,如何有效应对路径依赖约束问题,保障船舶航行的安全性和稳定性。/t/n提出了一种基于障碍李亚普诺夫函数(Barrier Lyapunov Function,BLF)的固定时间自适应控制策略,并结合自适应反步法设计了控制器,避免了传统控制器设计中的“复杂性爆炸”问题,增强了控制系统应对路径依赖约束的能力。通过仿真验证,采用智能船“大智”号船舶模型并模拟浅水效应环境,分析了控制器的性能。/t/n仿真结果显示,在不违反约束要求的前提下,路径跟踪误差能够快速收敛到预定区域,验证了控制器的有效性。/t/n提出的控制策略不仅能够应对船舶路径依赖约束问题,还能确保在固定时间内实现精确路径跟踪,具有理论和实际应用价值。

     

    Abstract: 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.

     

/

返回文章
返回