High precision control of industrial robot based on flexible joint response model

Abstract

For industrial robots, their repetition accuracy is high, but their absolute accuracy and trajectory accuracy are low, which limits the application of industrial robots in more scenarios. Current solutions mainly address precision issues caused by static errors through adjustments in kinematic parameters. Approaches such as dynamic feedforward control, torque computation methods, intelligent control, dual encoder control, visual servoing, etc., are employed to enhance dynamic tracking accuracy, yet they commonly suffer from insufficient precision, poor robustness, high costs, and usability challenges. In response to the difficulty in reducing dynamic errors, this paper proposes a high-precision control technique based on compensating for flexible joint responses. By compensating for deviations in flexible joint responses, the trajectory precision at the end-effector of industrial robots is improved.

Keywords:

• Manipulator
• trajectory precision
• flexible joints
• dynamics

Acknowledgments

Yunfei Qu, Xin Zhang, and Yunfei Qu contributed to the design and methodology of this study; YingKai Sun and Hong Wang contributed to the assessment of the outcomes, and the writing of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Shenzhen Science and Technology Innovation Commission through the Shenzhen General Project For Science and Technology Development under Grant JSGGZD20220822095000001.

Notes on contributors

Yunfei Qu

Yunfei Qu was born in Henan Province, China in 1988. He Received the M.S. degrees in Control theory and control engineering from Shanghai Jiao Tong University in 2014. Since 2014, he has been working at Inovance Technology Co., Ltd. in Shenzhen, focusing on research in areas such as robot dynamics, robot control methods, optimization of robot core components, robot system design and optimization.

Changhua Xu

Changhua Xu was born in Shandong Province, China in 1990. He Received the M.S. degrees in Mechanical Engineering from the Harbin Institute of Technology in 2015. Since 2015, he has been working at Inovance Technology Co., Ltd. in Shenzhen, focusing on research in areas such as robot trajectory planning, motion efficiency improvement, robot system design and optimization.

Xin Zhang

Xin Zhang was born in Guangxi Province, China in 1990. He Received the B.S., M.S. degrees in Mechanical Engineering from the Harbin Institute of Technology in 2014 and 2016, respectively. Since 2016, he has been working at Inovance Technology Co., Ltd. in Shenzhen, focusing on research in areas such as robotics dynamic control, robot collision detection, robot vibration suppression, and robot Kinematic error compensation.

Yingkai Sun

YingKai Sun was born in Heilongjiang, China, in 1999. He received the B.S. degree in Intelligent Science and Technology from the University of Science and Technology Beijing, Beijing, China, in 2021. He is currently pursuing the M.S. degree in Electrical Engineering at the Harbin Institute of Technology, Shenzhen, China. His research interests is control systems.

Hong Wang

Hong Wang was born in Heilongjiang, China, in 1975. He received the B.S., M.S., and Ph.D.degrees in electrical engineering from the Harbin Institute of Technology, Harbin, China, in 1998, 2000, and 2004, respectively. From 2008 to 2010, he was a Postdoctoral Fellow with the Shenzhen Graduate School, Harbin Institute of Technology, where he has been an Associate Professor, since 2010. His research interests include renewable energy systems, electric motor drive design, electric vehicle control techniques, and power electronics.