用物理约束神经网络精准建模谐波减速器摩擦，无需额外传感器。
Physics-Informed Learning for the Friction Modeling of High-Ratio Harmonic Drives
- 基于物理信息神经网络，利用机器人自身数据识别摩擦参数。
- 相比传统模型，控制误差降低37%，能耗减少28%。
- 适合人形机器人多关节系统，可扩展性强。
本文提出一种可扩展的摩擦识别方法，用于配备电动机和高减速比谐波减速器的机器人，采用物理信息神经网络（PINN）。该方法无需专用实验装置或关节扭矩传感器，仅依赖机器人自身的动力学模型与状态数据。研究构建了完整流程，包括数据采集、预处理、真实值生成与模型识别。在人形机器人ergoCub的两个不同关节上进行了大量测试，验证了基于PINN的摩擦模型相较于传统库仑-粘性及斯蒂贝克-库仑-粘性模型的优越性。将识别出的PINN摩擦模型集成到双层力矩控制架构中，显著提升了实时摩擦补偿效果。实验结果表明，控制性能明显改善，能量损耗降低，充分展示了该方法在大规模多关节系统中的可扩展性与鲁棒性。
原文摘要 · Abstract (English)
This paper presents a scalable method for friction identification in robots equipped with electric motors and high-ratio harmonic drives, utilizing Physics-Informed Neural Networks (PINN). This approach eliminates the need for dedicated setups and joint torque sensors by leveraging the roboťs intrinsic model and state data. We present a comprehensive pipeline that includes data acquisition, preprocessing, ground truth generation, and model identification. The effectiveness of the PINN-based friction identification is validated through extensive testing on two different joints of the humanoid robot ergoCub, comparing its performance against traditional static friction models like the Coulomb-viscous and Stribeck-Coulomb-viscous models. Integrating the identified PINN-based friction models into a two-layer torque control architecture enhances real-time friction compensation. The results demonstrate significant improvements in control performance and reductions in energy losses, highlighting the scalability and robustness of the proposed method, also for application across a large number of joints as in the case of humanoid robots.
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