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.

翻译：本文提出了一种混合物理-机器学习建模框架，用于水面运载器的操纵运动，以解决在环境扰动存在下的建模能力与稳定性问题。从深度学习视角看，该框架基于一种带有额外特征提取的残差网络变体。首先，推导并识别了一个不完美的物理模型，以捕捉海洋运载器的基本水动力特性。随后，该模型通过一个残差块与一个前馈网络相集成。此外，在机器学习组件中采用了来自三角变换的特征提取，以考虑海流和波浪的周期性影响。所提方法使用'JH7500'无人水面艇的真实航行数据进行了评估。结果表明，该非线性动力学模型在特定环境条件下具有稳健的泛化能力和精确的长期预测性能。该方法有潜力被扩展并应用于开发一个全面的高保真模拟器。