Comprehensive Dynamic Friction Identification and Compensation in Joints of Collaborative SCARA Robots

doi:10.3901/JME.2025.07.325

Abstract

Abstract: In the field of collaborative robot dynamics modeling, the presence of nonlinear friction forces at the joints results in low precision of force control based on motor feedback current. Against this backdrop, the research focuses on the nonlinear friction forces of collaborative robots to enhance the accuracy of their identification, thereby improving torque control performance. A new collaborative SCARA robot has been designed, analyzing the components of joint friction and conducting experiments on factors affecting friction, such as joint speed, temperature, and load. By collecting time series data of the robot’s position, speed, torque, ambient temperature, and load, the theoretical torque is calculated using the Newton-Euler method, the actual torque is used as the target for supervised learning to establish an Informer-LSTM-based parallel hybrid neural network model. This model identifies nonlinear composite dynamic friction forces with different time series characteristics, ultimately compensating for joint friction with feedforward torque. Experimental results show that the Informer-LSTM parallel hybrid neural network model can accurately predict long and short time series data. The data-driven comprehensive dynamic friction model is more precise at zero and very low speeds, with the average error in joint torque after friction compensation below 1%, significantly enhancing torque precision.

Key words: collaborative SCARA robot, nonlinear friction identification, friction compensation, long short-term memory, Informer

CLC Number:

TP242

WANG Pengpeng, LU Hao, YANG Zhiqiang, HOU Funing, GUO Shijie, GAN Zhongxue. Comprehensive Dynamic Friction Identification and Compensation in Joints of Collaborative SCARA Robots[J]. Journal of Mechanical Engineering, 2025, 61(7): 325-337.

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