Hostname: page-component-745bb68f8f-5r2nc Total loading time: 0 Render date: 2025-02-04T02:44:05.120Z Has data issue: false hasContentIssue false
  • English
  • Français

Zero-force drag and start-up torque compensation strategies for robots

Published online by Cambridge University Press:  31 January 2025

Chenggang Li Open the ORCID record for Chenggang Li [Opens in a new window] ,
Lujin Zhu ,
Gang Yang  and
Junxian Zhang
Chenggang Li*
Affiliation:
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Lujin Zhu
Affiliation:
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Gang Yang
Affiliation:
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Junxian Zhang
Affiliation:
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
*
Corresponding author: Chenggang Li; Email: lichenggang@nuaa.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

A method is proposed for identifying robot gravity and friction torques based on joint currents. The minimum gravity term parameters are obtained using the Modified Denavit–Hartenberg (MDH) parameters, and the dynamic equations are linearized. The robot’s friction torque is identified using the Stribeck friction model. Additionally, a zero-force drag algorithm is designed to address the issue of excessive start-up torque during dragging. A sinusoidal compensation algorithm is proposed to perform periodic friction compensation for each stationary joint, utilizing the identified maximum static friction torque. Experimental results show that when the robot operates at a uniform low speed, the theoretical current calculated based on the identified gravity and friction fits the actual current well, with a maximum root mean square error within 50 mA, confirming the accuracy of the identification results. The start-up torque compensation algorithm reduces the robot’s start-up torque by an average of $ 60.58\mathrm{\%}$, improving the compliance of the dragging process and demonstrating the effectiveness of the compensation algorithm.

Keywords

robotgravity identificationfriction identificationzero-force dragstart-up torque compensation
Type
Research Article
Information
Robotica , First View , pp. 1 - 19
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Villa, N., Mobedi, E. and Arash, A., “A Contact-Adaptive Control Framework for Co-Manipulation Tasks with Application to Collaborative Screwing,” 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (IEEE, 2022), pp. 1131–1137.CrossRefGoogle Scholar
Chen, S., Research on the Technology of Zero Moment Control and Collision Detection of Collaborative Robot (University of Science and Technology of China, 2018).Google Scholar
Wang, C. and Zhao, J., “Based on human-like variable admittance control for human-robot collaborative motion,” Robotica 41(7), 2155–2176 (2023).CrossRefGoogle Scholar
Wei, C., Research on Sensorless Zero-force Control of Collaborative Robots (Changchun University of Technology, 2023).Google Scholar
Fen, L., Research on Zero-force Control of Collaborative Robots Based on Dynamic Parameters Identification (Jiangnan University, 2022).Google Scholar
Zhu, D., Gan, Y. and Dai, X., “Research on Drag Control Algorithm of Cooperative Robot Based on Torque Compensation,” 2021 China Automation Congress (CAC) (IEEE, 2021), pp. 708–715.CrossRefGoogle Scholar
Lei, C., Huang, S., Zhong, Z. and He, J., “A Self Developed Cooperative Manipulator and Its Zero Force Drag Control,” 2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) (IEEE, 2022), pp. 521–526.CrossRefGoogle Scholar
Reyes-Uquillas, D. and Hsiao, T., “Safe and intuitive manual guidance of a robot manipulator using adaptive admittance control towards robot agility,” Rob. Comput. Integr. Manuf. 70, 102127 (2021).CrossRefGoogle Scholar
Wang, Y., Yang, Y., Zhao, B., Qi, X., Hu, Y., Li, B., Sun, L., Zhang, L. and Meng, M. Q.-H., “Variable admittance control based on trajectory prediction of human hand motion for physical human-robot interaction,” Appl. Sci. 11(12), 5651 (2021).CrossRefGoogle Scholar
Park, C., Kyung, J. H., Do, H. M. and Choi, T., “Development of Direct Teaching Robot System,” 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) (IEEE, 2011), pp. 730–732.CrossRefGoogle Scholar
Park, D. I., Park, C. and Kyung, J.-H., “Design and Analysis of Direct Teaching Robot for Human-Robot Cooperation,” 2009 IEEE International Symposium on Assembly and Manufacturing (IEEE, 2009), pp. 220–224.CrossRefGoogle Scholar
Park, D.-I., Park, C. and Kyung, J.-H., “Signal Processing of Direct Teaching Data for Human-Robot Cooperation,” 2010 IEEE International Symposium on Industrial Electronics (IEEE, 2010), pp. 1956–1960.CrossRefGoogle Scholar
Xu, J., Zhi, Z. and Dong, J., “Free-force control of flexible robot joint system without sensors on link side,” J. Zhejiang Univ. (Eng. Sci.) 54(7), 1256–1263 (2020).Google Scholar
Huynh, H. N., Assadi, H., Rivière-Lorphèvre, E., Verlinden, O. and Ahmadi, K., “Modelling the dynamics of industrial robots for milling operations,” Rob. Comput. Integr. Manuf. 61, 101852 (2020).CrossRefGoogle Scholar
Ding, Y.-D., Chen, B., Wu, H.-T. and Shen, H.-Y., “An identification method of industrial robot’s dynamic parameters,” J. South China Univ. Technol. (Nat. Sci. Ed.) 43 (3), 49–56 (2015).Google Scholar
Yu, C., Li, Z., Yang, D. and Liu, H., “A fast robotic arm gravity compensation updating approach for industrial application using sparse selection and reconstruction,” Rob. Auton. Syst. 149, 103971 (2022).CrossRefGoogle Scholar
Liu, G., Li, Q., Fang, L., Han, B. and Zhang, H., “A new joint friction model for parameter identification and sensor-less hand guiding in industrial robots,” Ind. Robot Int. J. Robot. Res. Appl. 47 (6), 847–857 (2020).CrossRefGoogle Scholar
Li, Z., Li, C., Ding, S., Li, P. and Yang, G., “Research on accurate identification of robot gravity and friction forces,” Mod. Mach. Tool Automat. Manufact. Techniq. (08), 1–5+12 (2024).Google Scholar
Tu, X., Research on the Key Technology of Industrial Robot Motion Control Based on Feedforward Dynamics (Huazhong University of Science and Technology, 2018).Google Scholar
Qian, H., Li, C., Du, Z., Li, M., Ding, S., Li, P. and Li, Z., “Iterative identification of robot dynamic parameters based on logistic function,” Trans. Nanjing Univ. Aeronaut. Astronaut. 39(6), 684–695 (2023).Google Scholar
Qin, Z., Baron, L. and Birglen, L., “A new approach to the dynamic parameter identification of robotic manipulators,” Robotica 28(4), 539–547 (2010).CrossRefGoogle Scholar
Liu, C.-H., Yao, B.-G. and Ke, Y.-L., “Study on off-line programming of industrial robot for cutting process,” J. Zhejiang Univ. (Eng. Sci.) 44(3), 426–431 (2010).Google Scholar
Li, M., Research on the Technology of Zero Force Control and Direct Teaching of Collaborative Robot (Huazhong University of Science and Technology, 2021).Google Scholar
Zou, K.-J. and Ding, J.-W., “Research on dynamic parameter identification of robot,” Mod. Mach. Tool Automat. Manufact. Techniq. (5), 10–13+\18 (2023).Google Scholar
Deng, Z., Liu, N., Min, F. and Wang, G., “Remote controller assembly strategy based on degrees offreedom constraint and sinusoidal friction compensation,” Comput. Integr. Manufacturing Syst. 29(5), 1429 (2023).Google Scholar
Qi, M., Research on Drag Teaching Technology of Six-axis Manipulator Based on Current Feedback (Huazhong University of Science and Technology, 2022).Google Scholar
Iwatani, M. and Kikuuwe, R., “Some improvements in elastoplastic friction compensator,” SICE J. Control Meas. Syst. Integr. 10(3), 141–148 (2017).CrossRefGoogle Scholar
Xiao, Q., Research on Robot Drag Teaching Technology Based on Dynamic Parameter Identification (Huazhong University of Science and Technology, 2021).Google Scholar
Hong, J., Manual Dragging Teaching and Collision Detection Based on Robot Dynamics Model (South China University of Technology, 2020).Google Scholar
Iwatani, M. and Kikuuwe, R., “An Elastoplastic Friction Compensator with Improved Static Friction Behavior,” 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) (IEEE, 2016), pp. 1091–1097.CrossRefGoogle Scholar