融合物理模型与数据驱动，提升柔性机械臂动力学建模精度
Hybrid Dynamics Modeling for a Flexible 2-DoF Robotic Arm
- 结合刚体动力学与高斯混合模型，捕捉残差误差和柔性效应
- 正则化回归比纯参数模型更贴近实测扭矩，误差更低
- 适合关注柔性机器人建模与半参数学习的研究者
本文研究三种柔性双自由度机械臂动力学建模方法，以解决刚体模型无法捕捉的未建模动态。两种基于物理的方法将刚体动力学（RBD）与高斯混合模型（GMM）结合，用于表征残差误差和连杆柔性；一种基于运动学的回归模型作为纯数据驱动基线。利用开源数据集，首先通过岭回归在运动学特征上估计力矩，物理基线采用文献中公布的参数，随后使用普通最小二乘法直接从数据估计同一参数集。结果表明，基于物理的参数精度最差，而正则化与最小二乘估计更接近实测力矩。残差分析与误差指标揭示了纯参数模型在柔性系统中的局限性，凸显正则化与数据驱动辨识的重要性，支持半参数残差学习方法的发展。
原文摘要 · Abstract (English)
This paper examines three approaches for modeling the dynamics of a flexible-link 2-DoF robotic arm to address unmodeled dynamics not captured by rigid-body models. Two physics informed models combine rigid-body dynamics (RBD) formulations with a Gaussian Mixture Model (GMM) to capture residual model errors and linkage flexibility. A kinematics-based regression model serves as a purely data-driven baseline. Using an open-source dataset, torque predictions are first estimated using Ridge regression on kinematic features, while the physicsbased baseline is constructed from published specifications, and ordinary least-squares regression is subsequently used to estimate the same parameter set directly from data. Results show that the physics-based parameters yield the poorest accuracy, while regularized and least-squares estimators align more closely with measured torques. Residual analysis and error metrics highlight the limitations of purely parametric models for flexible-link systems and underscore the value of regularization and data-driven identification, supporting developments of semi-parametric residual learning methods.
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