Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-24T12:04:35.306Z Has data issue: false hasContentIssue false
  • English
  • Français

A novel parameter identification method for flexible-joint robots using input torque and motor-side motion data

Published online by Cambridge University Press:  14 February 2022

Pu Zhao Open the ORCID record for Pu Zhao [Opens in a new window]
Pu Zhao*
Affiliation:
School of Mechanical and Electrical Engineering, Henan University of Technology, Zheng Zhou 450001, China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The traditional identification methods of industrial robots are based on Inverse Dynamic Identification Model (IDIM). Based on the model, input torque, motor-side and link-side motion data are necessary when joint flexibilities are considered. However, it is often unavailable or expensive to general robots which are not equipped with link-side sensors. To solve the problem, a novel dynamic parameter identification method, which only employ input torque and motor-side motion data, is proposed in this report. Based on motor-side dynamics, link-side dynamics are modified as high-order nonlinear functions of input torque and motor-side motion. Then, through different trajectory-load groups and high-order observers, the nonlinear equations can be solved, and dynamic parameters can be estimated with short operation time. The selection rules of the trajectory-load groups are then discussed based on simulation results, so as to promote estimation results. Finally, experiments are conducted to verify the proposed method and exhibit the selection rules of observer gains. As shown in the report, except viscous friction parameters, identification difference between the IDM-based methods and the proposed one is less than 9%.

Keywords

flexible-joint robotdynamic modelobserverparameter identificationnonlinear equations
Type
Research Article
Information
Robotica , Volume 40 , Issue 9 , September 2022 , pp. 3077 - 3087
Copyright
© The Author(s), 2022. 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

Wang, Y., Jiang, S., Chen, B. and Wu, H., “Trajectory tracking control of underwater vehicle-manipulator system using discrete time delay estimation,” IEEE Access 5, 7435–7443 (2017).CrossRefGoogle Scholar
Zeng, F., Xiao, J. and Liu, H., “Force/torque sensorless compliant control strategy for assembly tasks using a 6-DOF collaborative robot,” IEEE Access 7, 108795–108805 (2019).CrossRefGoogle Scholar
Wu, J., Wang, J. and You, Z., “An overview of dynamic parameter identification of robots,” Rob. Comput. Integr. Manuf. 26(5), 414–419 (2010).CrossRefGoogle Scholar
Siciliano, B. and Khatib, O., Springer Handbook of Robotics (Springer, 2016).CrossRefGoogle Scholar
Park, K.-J., “Fourier-based optimal excitation trajectories for the dynamic identification of robots,” Robotica 24(5), 625–633 (2006).CrossRefGoogle Scholar
Gautier, M., Vandanjon, P.-O. and Janot, A., “Dynamic Identification of a 6 Dof Robot without Joint Position Data,” 2011 IEEE International Conference on Robotics and Automation (IEEE, 2011) pp. 234–239.CrossRefGoogle Scholar
Brunot, M., Janot, A., Young, P. C. and Carrillo, F., “An improved instrumental variable method for industrial robot model identification,” Control Eng. Practice 74, 107–117 (2018).CrossRefGoogle Scholar
Jia, J., Zhang, M., Zang, X., Zhang, H. and Zhao, J., “Dynamic parameter identification for a manipulator with joint torque sensors based on an improved experimental design,” Sensors 19(10), 2248 (2019).CrossRefGoogle Scholar
Zollo, L., Lopez, E., Spedaliere, L., Garcia Aracil, N. and Guglielmelli, E., “Identification of dynamic parameters for robots with elastic joints,” Adv. Mech. Eng. 7(2), 843186 (2015).CrossRefGoogle Scholar
Miranda-Colorado, R. and Moreno-Valenzuela, J., “Experimental parameter identification of flexible joint robot manipulators,” Robotica 36(3), 313–332 (2018).CrossRefGoogle Scholar
Gautier, M., Janot, A., Jubien, A. and Vandanjon, P.-O., “Joint Stiffness Identification from only Motor Force/Torque Data,” 2011 50th IEEE Conference on Decision and Control and European Control Conference (IEEE, 2011) pp. 5088–5093.CrossRefGoogle Scholar
Fagiolini, A., Trumić, M. and Jovanović, K., “An input observer-based stiffness estimation approach for flexible robot joints,” IEEE Rob. Autom. Lett. 5(2), 1843–1850 (2020).Google Scholar
Ni, H., Zhang, C., Hu, T., Wang, T., Chen, Q. and Chen, C., “A dynamic parameter identification method of industrial robots considering joint elasticity,” Int. J. Adv. Rob. Syst. 16(1), 1729881418825217 (2019).Google Scholar
Jubien, A., Gautier, M. and Janot, A., “DIDIM-CLIE Method for Dynamic Parameter Identification of Flexible Joint Robots,” 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (IEEE, 2015) pp. 226–231.CrossRefGoogle Scholar
Jin, J. and Gans, N., “Parameter identification for industrial robots with a fast and robust trajectory design approach,” Rob. Comput. Integr. Manuf. 31, 21–29 (2015).CrossRefGoogle Scholar
Talole, S. E., Kolhe, J. P. and Phadke, S. B., “Extended-state-observer-based control of flexible-joint system with experimental validation,” IEEE Trans. Ind. Electron. 57(4), 1411–1419 (2009).CrossRefGoogle Scholar