Robust Estimation for an Extended Dynamic Parameter Set of Serial Manipulators and Unmodeled Dynamics Compensation
Shifeng Huang, Jihong Chen, Jianwei Zhang, Zhihong Zhu, Huicheng Zhou, Fan Li, Xing Zhou
Abstract
Advanced robotic applications have revived interest in identification of a high-precision dynamic model. In this article, we propose an extended dynamic parameter set (EDS). The EDS breaks through the limitation that the base dynamic parameter set needs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of the gravity direction for modeling. Moreover, we present a novel parameters identification technique (RSIH), which is a complete solution and can significantly mitigate negative effects of the measurement noise and outliers. Besides, an incremental learning technique combined with a compensation limit criterion is employed to compensate for unmodeled dynamics. Simulations and experiments demonstrate the EDS-based model can adapt to any installation angle of a base plate, and confirm the RSIH technique outperforms the widely used identification techniques in industry and is equal to or even better than the state-of-the-art physical feasibility technique in terms of identification precision and robustness. In addition, the modeling errors, especially the uncertainty of the friction model, can be greatly compensated.