TY - GEN
T1 - A WLS-Based Step-by-Step Identification Method for Industrial Robot Dynamics Parameters
AU - Wang, Qianjiang
AU - Kong, Mingxiu
AU - Bian, Chuancheng
AU - Hong, Hao
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In order to improve the accuracy of robot dynamics parameter identification, this paper designs a step-by-step identification method based on weighted least squares. The robot dynamics model is first linearized to obtain the minimum parameter set. Subsequently, the friction model is built separately and experiments are designed to recognize the joint friction parameters first. Based on this, the excitation trajectory of finite term Fourier series is designed and optimized and data are collected. The joint moments obtained from the difference between the overall experimentally collected moments and the calculated friction moments are used to obtain the complete robot dynamics parameters based on weighted least squares identification. Comparison of the designed overall identification experiments shows that the total moment residuals of this method are reduced by 35.52% compared with the one-time identification method, which proves the effectiveness of this method.
AB - In order to improve the accuracy of robot dynamics parameter identification, this paper designs a step-by-step identification method based on weighted least squares. The robot dynamics model is first linearized to obtain the minimum parameter set. Subsequently, the friction model is built separately and experiments are designed to recognize the joint friction parameters first. Based on this, the excitation trajectory of finite term Fourier series is designed and optimized and data are collected. The joint moments obtained from the difference between the overall experimentally collected moments and the calculated friction moments are used to obtain the complete robot dynamics parameters based on weighted least squares identification. Comparison of the designed overall identification experiments shows that the total moment residuals of this method are reduced by 35.52% compared with the one-time identification method, which proves the effectiveness of this method.
KW - dynamics model
KW - parameter identification
KW - robot
KW - stepwise identification
KW - weighted least squares
UR - https://www.scopus.com/pages/publications/85198221509
U2 - 10.1109/ICCAR61844.2024.10569278
DO - 10.1109/ICCAR61844.2024.10569278
M3 - 会议稿件
AN - SCOPUS:85198221509
T3 - 2024 10th International Conference on Control, Automation and Robotics, ICCAR 2024
SP - 172
EP - 178
BT - 2024 10th International Conference on Control, Automation and Robotics, ICCAR 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Control, Automation and Robotics, ICCAR 2024
Y2 - 27 April 2024 through 29 April 2024
ER -