Parameter Identification Of Robotic Arm Dynamics Model Based On Multi-Objective Particle Swarm Algorithm

To enhance the precision of the robotic arm dynamics model and ensure the consistency of unidentified parameters across various operating conditions, a parameter identification method based on the multi-objective particle swarm algorithm is proposed. Using the LuGre friction model, the dynamics model of the robotic arm are established. Parameters are identified by minimizing the difference between simulated and actual displacements under 30°and 50°conditions. Verification is conducted at 40°, demonstrating reduced mean square errors of the angular displacement curves by 9.31% and 9.10% for the initial two conditions. These results confirm the effectiveness of the parameter identification method in improving the model's accuracy.