TY - JOUR
TI - Robot parameter identification and redundancy analysis based on adaptive ridge regression
AU - Kang Yonggang
AU - Li Guomao
AU - Kou Shuaijia
AU - Yu Wentao
AU - Song Siren
AU - Wang Zihao
JN - Thermal Science
PY - 2025
VL - 29
IS - 3
SP - 2095
EP - 2104
PT - Article
AB - This paper proposes an iterative parameter identification algorithm based on  adaptive ridge regression to establish a geometric error model for improving  the absolute positioning accuracy of the end-effector of collaborative  robots. The perturbation method is employed to establish the aforementioned  model. This algorithm addresses the overfitting and lack of regularization  issues associated with the least squares method under multicollinearity and  high-dimensional data, thereby enhancing the model generalization  capability. A parameter redundancy analysis was conducted on the positional  error model for multi-degree-of-freedom collaborative robots. The  experimental results demonstrate that the elimination of redundant  parameters through analytical methods improves the reliability and accuracy  of parameter identification and enhances the model robustness. In  comparison to the least squares method, the proposed algorithm demonstrates  superior identification accuracy and generalization capability, resulting  in a notable enhancement in the absolute positioning accuracy of  collaborative robots through calibration.