用在线学习提升微型无人船的精准控制能力
Dynamic Modeling and Efficient Data-Driven Optimal Control for Micro Autonomous Surface Vehicles
- 结合物理模型与数据驱动，实时优化动力学模型
- 在未知载荷和干扰下仍保持高轨迹跟踪精度
- 适合需要高可靠性的小型水面机器人应用
微型自主水面航行器（MicroASVs）在狭小水域和集群机器人任务中具有重要应用潜力。然而，由于非线性水动力建模复杂、自运动效应敏感及环境扰动（如波浪和边界效应）影响，小型尺度下的精确鲁棒控制仍具挑战。本文提出一种面向过驱动微型航船的物理驱动动力学模型，并设计基于弱形式的在线模型学习方法的数据驱动最优控制框架。该方法可实时持续修正物理模型，实现对变化系统参数的自适应控制。仿真结果表明，所提方法显著提升了轨迹跟踪精度与鲁棒性，即使在未知载荷和外部干扰下依然表现优异。研究验证了数据驱动在线学习在提升微型无人船性能方面的潜力，为更可靠、精准的自主水面航行提供新路径。
原文摘要 · Abstract (English)
Micro Autonomous Surface Vehicles (MicroASVs) offer significant potential for operations in confined or shallow waters and swarm robotics applications. However, achieving precise and robust control at such small scales remains highly challenging, mainly due to the complexity of modeling nonlinear hydrodynamic forces and the increased sensitivity to self-motion effects and environmental disturbances, including waves and boundary effects in confined spaces. This paper presents a physics-driven dynamics model for an over-actuated MicroASV and introduces a data-driven optimal control framework that leverages a weak formulation-based online model learning method. Our approach continuously refines the physics-driven model in real time, enabling adaptive control that adjusts to changing system parameters. Simulation results demonstrate that the proposed method substantially enhances trajectory tracking accuracy and robustness, even under unknown payloads and external disturbances. These findings highlight the potential of data-driven online learning-based optimal control to improve MicroASV performance, paving the way for more reliable and precise autonomous surface vehicle operations.
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