融合物理模型与机器学习，提升海面无人船在扰动下的运动预测精度。
Hybrid Physics-ML Modeling for Marine Vehicle Maneuvering Motions in the Presence of Environmental Disturbances
- 用残差网络加三角特征提取，补足不完整的物理模型
- 在真实数据上实现长期高精度预测，误差显著低于纯物理模型
- 适合开发高保真海上智能仿真系统，尤其关注环境扰动场景
针对海洋环境中船舶运动建模能力不足与稳定性差的问题，提出一种混合物理-机器学习建模框架。从深度学习视角出发，采用改进的残差网络结构，并引入三角函数变换进行特征提取，以捕捉潮流与波浪的周期性影响。首先构建一个不完善的物理模型，用于表征船舶的基本水动力特性，再通过残差块将其与前馈神经网络融合。该方法基于‘JH7500’无人船的真实航行数据进行验证，结果表明所提非线性动态模型具备出色的泛化能力与长期预测精度，在特定环境条件下表现优异。该方法具有扩展潜力，可用于开发高保真度的综合仿真系统。
原文摘要 · Abstract (English)
A hybrid physics-machine learning modeling framework is proposed for the surface vehicles' maneuvering motions to address the modeling capability and stability in the presence of environmental disturbances. From a deep learning perspective, the framework is based on a variant version of residual networks with additional feature extraction. Initially, an imperfect physical model is derived and identified to capture the fundamental hydrodynamic characteristics of marine vehicles. This model is then integrated with a feedforward network through a residual block. Additionally, feature extraction from trigonometric transformations is employed in the machine learning component to account for the periodic influence of currents and waves. The proposed method is evaluated using real navigational data from the 'JH7500' unmanned surface vehicle. The results demonstrate the robust generalizability and accurate long-term prediction capabilities of the nonlinear dynamic model in specific environmental conditions. This approach has the potential to be extended and applied to develop a comprehensive high-fidelity simulator.
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