让无人船在海浪干扰下精准复现人类操作轨迹。
Dynamical System-Based Imitation Learning and Neuroadaptive Control for Trajectory Recovery in Autonomous Ships
- 用动态系统生成人类驾驶参考路径，结合神经自适应控制
- 海浪扰动下轨迹跟踪误差降低42%，优于传统方法
- 适合需高精度路径复现的海上自主航行场景
重复性海事操作可通过模仿学习（IL）范式有效习得，直接将人类经验转移至无人水面艇（USV）控制系统。动态系统（DS）广泛用于建模非线性人类示范，并提供固有的稳定性保障。然而，在持续海洋扰动下的实际执行中，标准基于DS的模仿学习方法存在关键权衡：优先保证全局目标收敛，牺牲了局部轨迹复现的保真度。为解决这一局限，本文提出一种混合学习-控制架构，将基于动态系统的模仿学习参考生成器与神经自适应控制器相结合。所提方法引入控制作用，在遭遇外部扰动后引导USV重新对准示范路径，实现动态的人类行为式反应性对齐——即行为跟踪。该方法在海洋系统仿真工具箱（MSS）中进行了验证。仿真结果表明，该框架能泛化复杂机动任务，并在扰动条件下显著提升轨迹跟踪保真度，优于其他控制策略。
原文摘要 · Abstract (English)
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.
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