An Accurate Dynamic Model Identification Method for Industrial Robots Based on Improved Excitation Trajectory

This article focuses on dynamic parameter identification for industrial robots and proposes a parameter identification method based on an improved excitation trajectory. First, a complex nonlinear friction model is adopted and modified according to joint friction characteristics, with a genetic algorithm utilized to determine its six parameters. Second, a weighted optimal excitation trajectory is designed to address nonlinear friction requirements and smooth operation constraints. Then, a global parameter optimization algorithm based on the least squares method and the modified sparrow search algorithm is proposed. Finally, the proposed method is validated on a self-developed six-axis industrial robot. Experimental results demonstrate that the proposed method achieves higher identification accuracy compared with two representative identification approaches.