基于动力学系统的模仿学习与神经自适应控制在自主船舶轨迹恢复中的应用
Dynamical System-Based Imitation Learning and Neuroadaptive Control for Trajectory Recovery in Autonomous Ships
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中文总结 AI 辅助
该研究针对自主船舶轨迹恢复问题，提出融合基于动力学系统的模仿学习参考生成器与神经自适应控制器的混合架构，经海洋系统模拟器验证，可提升扰动下的轨迹跟踪保真度并泛化复杂机动任务。
中文摘要 AI 辅助
重复的海事作业可通过模仿学习（IL）范式有效学习，该范式将人类专业知识直接迁移至无人水面艇（USV）控制系统。动力学系统（DS）被广泛用于对非线性人类演示进行建模，同时提供固有的稳定性保证。然而，在持续的海洋扰动下进行实际执行时，会暴露出一个关键的权衡问题：标准基于DS的IL方法优先考虑全局目标收敛，却牺牲了局部轨迹复现保真度。为解决这一局限，本文提出一种混合学习-控制架构，将基于DS的IL参考生成器与神经自适应控制器相集成。该方法引入一种控制动作，在外部扰动下驱动USV回到演示路径，实现类似人类的动态反应性对齐——即行为跟踪。所提方法通过海洋系统模拟器（MSS）工具箱进行验证，仿真结果证实，与其他控制策略相比，该框架可泛化复杂机动任务，同时在扰动下大幅提升轨迹跟踪保真度。
英文摘要
Repetitive maritime operations can be effectively learned using the Imitation Learning (IL) paradigm, which transfers human expertise directly to Unmanned Surface Vehicle (USV) control systems. Dynamical Systems (DS) are widely used to model non-linear human demonstrations while offering inherent stability guarantees. However, real-world execution under persistent marine perturbations reveals a critical trade-off: standard DS-based IL approaches prioritize global target convergence at the expense of localized trajectory reproduction fidelity. To address this limitation, we present a hybrid learning-control architecture that integrates a DS-based IL reference generator with a neuroadaptive controller. Our approach introduces a control action that drives the USV back to the demonstrated path following exogenous disturbances, enabling dynamic human-like reactive alignment-termed behavioral tracking. The proposed methodology is validated using the Marine Systems Simulator (MSS) toolbox. Simulation results confirm that the framework generalizes complex maneuvering tasks while substantially improving trajectory tracking fidelity under disturbances compared to alternative control strategies.
发表机构
- University of Seville（塞维利亚大学）
- CUN（哥伦比亚国立大学（昆迪纳马卡））
机构由 AI 辅助整理，请以论文原文为准。
