对比物理约束神经网络，发现牛顿法更适合电机扭矩估计的逆动力学建模。
Newtonian and Lagrangian Neural Networks: A Comparison Towards Efficient Inverse Dynamics Identification
- 基于牛顿-欧拉与拉格朗日力学构建物理信息神经网络
- 在电机扭矩估计场景下，牛顿网络性能优于拉格朗日网络
- 适用于工业机器人逆动力学建模，尤其关注摩擦等耗散力建模
精确的逆动力学模型对工业机器人控制至关重要。近期研究将神经网络回归与牛顿-欧拉及欧拉-拉格朗日运动方程的逆形式结合，分别形成牛顿神经网络和拉格朗日神经网络。这些物理信息模型旨在从数据中识别解析方程中的未知参数。尽管潜力巨大，现有文献缺乏在拉格朗日与牛顿网络间选择的指导。本研究发现，当使用电机扭矩估计而非直接测量关节扭矩时，拉格朗日网络表现不如牛顿网络，因其未显式建模耗散扭矩。模型性能在MABI MAX 100工业机器人数据上进行了对比验证。
原文摘要 · Abstract (English)
Accurate inverse dynamics models are essential tools for controlling industrial robots. Recent research combines neural network regression with inverse dynamics formulations of the Newton-Euler and the Euler-Lagrange equations of motion, resulting in so-called Newtonian neural networks and Lagrangian neural networks, respectively. These physics-informed models seek to identify unknowns in the analytical equations from data. Despite their potential, current literature lacks guidance on choosing between Lagrangian and Newtonian networks. In this study, we show that when motor torques are estimated instead of directly measuring joint torques, Lagrangian networks prove less effective compared to Newtonian networks as they do not explicitly model dissipative torques. The performance of these models is compared to neural network regression on data of a MABI MAX 100 industrial robot.
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