Skip to main content

Experimental parameter identification of flexible joint robot manipulators

  • Roger Miranda-Colorado (a1) and Javier Moreno-Valenzuela (a2)

This paper contributes by presenting a parameter identification procedure for n-degrees-of-freedom flexible joint robot manipulators. An advantage of the given procedure is the obtaining of robot parameters in a single experiment. Guidelines are provided for the computing of the joint position filtering and velocity estimation. The method relies in the filtered robot model, for which no acceleration measurements are required. The filtered model is expressed in regressor form, which allows applying a parameter identification procedure based on the least squares algorithm. In order to assess the performance of the proposed parameter identification scheme, an implementation of a least squares with forgetting factor (LSFF) parameter identification method is carried out. In order to assess the reliability of the tested identification schemes, a model-based trajectory tracking controller has been implemented twice in different conditions: one control experiment using the estimated parameters provided by the proposed scheme, and another experiment using the parameters given by the LSFF method. These real-time control experiments are compared with respect to numerical simulations using the estimated parameters for each identification method. For the proposed scheme, the comparison between experiments and numerical simulations indicates better accuracy in the torque and position prediction.

Corresponding author
*Corresponding author. E-mail:
Hide All
1. Pratt G. A. and Williamson M. M., “Series Elastic Actuators,“ Intelligent Robots and Systems 95. ‘Human Robot Interaction and Cooperative Robots’, Proceedings. IEEE/RSJ International Conference, Pittsburg, USA (Aug. 5–9, 1995) pp. 399–406, doi: 10.1109/IROS.1995.525827.
2. Albu-Schäffer A. and Hirzinger G., “Parameter Identification and Passivity Based Joint Control for a 7DOF Torque Controlled Light Weight Robot,“ Proceedings of the IEEE International Conference on Robotics & Automation, Seoul, Korea (May 21–26, 2001) pp. 2852–2858, doi: 10.1109/ROBOT.2001.933054.
3. Siciliano B., Sciavicco L., Villani L. and Oriolo G., Robotics: Modeling, Planing and Control, 3rd ed. (Springer, London, 2008) doi: 10.1007/978-1-84628-642-1.
4. Albu-Schäffer A. and Hirzinger G., “State Feedback Controller for Flexible Joint Robots: A Globally Stable Approach Implemented on DLR's Light-Weight Robots,“ Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems, Takamatsu, Japan (Nov. 2000) pp. 1087–1093, doi: 10.1109/IROS.2000.893164.
5. Brogliato B., Ortega R. and Lozano R., “Global tracking controllers for flexible-joint manipulators: A comparative study,“ Automatica 31 (7), 941956 (1995), doi: 10.1016/0005-1098(94)00172-F.
6. Jiang Z. H. and Shinohara K., “Workspace Trajectory Tracking Control of Flexible Joint Robots Based on Backstepping Method,“ Proceedings of the IEEE Region 10 Conference (TENCON), Singapore (2016) pp. 3473–3476, doi: 10.1109/TENCON.2016.7848700.
7. Korayem A. H., Rahagi M. I., Babaee H. and Korayem M. H., “Maximum load of flexible joint manipulators using nonlinear controllers,“ Robotica 35 (1), 119142 (2017) doi: 10.1017/S0263574715000028.
8. Ott C., Albu-Schäffer A., Kugi A. and Hirzinger G., “On the passivity-based impedance control of flexible joint robots,“ IEEE Trans. Robot. 24 (2), 416429 (2008) doi: 10.1109/TRO.2008.915438.
9. Albu-Schäffer A., Ott C. and Hirzinger G., “A unified passivity-based control framework for position, torque and impedance control of flexible joint robots,“ Int. J. Robot. Res. 26 (1), 2339 (2007) doi: 10.1177/0278364907073776.
10. Lim S. Y., Dawson D. M., Hu J. and de Queiroz M. S., “An adaptive link position tracking controller for rigid-link flexible-joint robots without velocity measurements,“ IEEE Trans. Syst. Man Cybern.-Part B: Cybern. 27 (3), 412427 (1997) doi: 10.1109/3477.584949.
11. Huang A. C. and Chen Y. C., “Adaptive sliding control for single-link flexible-joint robot with mismatched uncertainties,“ IEEE Trans. Control Syst. Technol. 12 (5), pp. 770775 (2004) doi: 10.1109/TCST.2004.826968.
12. Lozano R. and Brogliato B., “Adaptive control of robot manipulators with flexible joints,“ IEEE Trans. Autom. Control 37 (2), 174181 (1992) doi: 10.1109/9.121619.
13. Khorasani K., “Adaptive control of flexible-joint robots,“ IEEE Trans. Robot. Autom. 8 (2), 250267 (1992) doi: 10.1109/70.134278.
14. Liu H., Huang Y. and Wu W., “Improved Adaptive Output Feedback Controller for Flexible-Joint Robot Manipulators,“ Proceedings of the IEEE International Conference on Information and Automation (ICIA), Ningbo, China (Aug. 1–3, 2016) pp. 1653–1658, doi: 10.1109/ICInfA.2016.7832083.
15. Raouf F., Mohamad S., Maarouf S. and Maamar B.Distributed adaptive control strategy for flexible link manipulators,“ Robotica 123 (2016) doi: 10.1017/S0263574716000448.
16. Zouari L., Abid H. and Abid M., “Sliding mode and PI controllers for uncertain flexible joint manipulator,“ Int. J. Autom. Comput. 12 (2), 117124 (2015) doi: 10.1007/s11633-015-0878-x.
17. Akyüz I. H., Bingül Z. and Kizir S., “Cascade fuzzy logic control of a sinlge-link-flexible-joint manipulator,“ Turk. J. Electr. Eng. Comput. Sci. 20 (5), (2012) doi: 10.3906/elk-1101-1056.
18. Agee J. T., Bingül Z. and Kizir S., “Higher-order differential feedback control of a flexible-joint manipulator,“ J. Viber. Control 21 (10), 19761986 (2013) doi: 10.1177/1077546313504979.
19. Leahy M. B. and Saridis G. N., “Compensation of industrial manipulator dynamics,“ Int. J. Robot. Res. 8, 7384 (1989) doi: 10.1177/027836499000900406.
20. Caccavale F. and Chiacchio P., “Identification of dynamic parameters and feedforward control for a conventional industrial manipulator,“ Control Eng. Pract. 2 (6), 10391050 (1994) doi: 10.1016/0967-0661(94)91626-8.
21. Swevers J., Verdonck W. and Schutter J. D., “Dynamic model identification for industrial robots,“ IEEE Control Syst. Mag. 27 (5), 5871 (2007) doi: 10.1109/MCS.2007.904659.
22. Bingül Z. and Karahan O., “Dynamic identification of Staubli RX-60 robot using PSO and LS methods,“ Expert Syst. Appl. 38, 41364149 (2011) doi: 10.1016/j.eswa.2010.09.076.
23. Wu J., Wang J. and You Z., “An overview of dynamic parameter identification of robots,“ Robot. Comput.-Integr. Manuf. 26, 414419 (2010) doi: 10.1016/j.rcim.2010.03.013.
24. Swevers J., Ganseman C., Tukel D. B., Schutter J. D. and Brussel H. V., “Optimal robot excitation and identification,“ IEEE Trans. Robot. Autom. 13 (5), 730740 (1997) doi: 10.1109/70.631234.
25. Pham M. T., Gautier M. and Poignet P., “Identification of Joint Stiffness with Band Pass Filtering,“ Proceedings of the 2001 IEEE International Conference on Robotics & Automation, Seoul Korea (May 21–26, 2001) doi: 10.1109/ROBOT.2001.933056.
26. Lightcap C. and Banks S., “Dynamic Identification of a Mitsubishi PA10-6CE Robot using Motion Capture,“ Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA (Oct. 29–Nov. 2, 2007) doi:10.1109/IROS.2007.4399425.
27. Ruderman M., Hoffmann F. and Bertram T., “Modeling and identification of elastic robot joints with hysteresis and backlash,“ IEEE Trans. Indust. Electron. 56 (10), (2009) doi: 10.1109/TIE.2009.2015752.
28. van Zutven P., Kostić D. and Nijmeijer H., “Parameter Identification of Robotic Systems with Series Elastic Actuators,“ Proceedings of the 8th IFAC Symposium on Nonlinear Control Systems, Bologna, Italy (2010) pp. 350–355, doi: 10.3182/20100901-3-IT-2016.00127.
29. Gaz C., Flacco F. and De Luca A., “Identifying the Dynamic Model used by the KUKA LWR: A Reverse Engineering Approach,“ Proceedings of the IEEE International Conference on Robotics and Automation, Hong Kong (May 31–Jun. 7, 2014) pp. 1386–1392, doi: 10.1109/ICRA.2014.6907033.
30. Zollo L., Lopez E., Spedaliere L., Aracil N. G. and Guglielmelli E., “Identification of dynamic parameters for robots with elastic joints,“ Adv. Mech. Eng. 7 (2), (2015) doi: 10.1155/2014/843186.
32. Canudas C., Siciliano B. and Bastin G., Theory of Robot Control (Springer Verlag, London, 1996) doi: 10.1007/978-1-4471-1501-4.
33. Spong M. W., “Modeling and control of elastic joint robots,“ J. Dyn. Sys. Meas. Control 109 (4), pp. 310318 (1987) doi: 10.1115/1.3143860.
34. Craig J. J., Introduction to Robotics: Mechanics and Control, 3rd ed. (Prentice Hall, 2004) ISBN: 978-0201543612.
35. Miranda R., Cinemática y Dinámica de Robots Manipuladores (Alfaomega, 2016) ISBN: 978-607-622-048-1.
36. Kelly R., Santibañez V. and Loría A., Control of Robot Manipulators in Joint Space (Springer Verlag, London, 2005) doi: 10.1007/b135572.
37. Sciavicco L. and Siciliano B., Modeling and Control of Robot Manipulators, 2nd ed. (McGraw-Hill, London: Springer-Verlag, 2000) doi: 10.1007/978-1-4471-0449-0.
38. Moreno-Valenzuela J. and Campa R., “Two classes of velocity regulators for input-saturated motor drives,“ IEEE Trans. Indust. Electron. 56 (6), (2009) doi: 10.1109/TIE.2009.2016515.
39. Khalil W. and Dombre E., Modeling, identification and Control of Robots, 3rd ed. (Taylor & Francis, Bristol, 2002) ISBN: 978-1-903996-66-9.
40. Atkeson C. G., An C. H. and Hollerbach J. M., “Estimation of inertial parameters of manipulator loads and links,“ Int. J. Robot. Res. 5 (3), pp. 101119 (1986) doi: 10.1177/027836498600500306.
41. De Luca A. and Book W., “Robots with Flexible Elements,“ In: Springer Handbook of Robotics (Siciliano B. and Khatib O., eds.) (Springer, 2008) pp. 287319, doi: 10.1007/978-3-540-30301-5.
42. Reyes F. and Kelly R., “Experimental evaluation of identification schemes on a direct drive robot,“ Robotica. 15 (5), 563571 (1997) doi: 10.1017/S0263574797000659.
43. Chan S. and Chen H., “An Efficient Algorithm for Identification of SCARA Robot Parameters Including Drive Characteristics,“ Proceedings of the 25th Annual Conference of the IEEE Industrial Electronics Society, San Jose, CA, USA (1999) pp. 1014–1019, doi: 10.1023/A:1013918927148.
44. Iagnemma G. L., Dubowsky S. and Morel G., “A Base Force/Torque Sensor Approach to Robot Manipulators Inertial Parameter Estimation,“ Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, Belgium (May 16–20, 1998) pp. 3316–3321, doi: 10.1109/ROBOT.1998.680950.
45. Dorf R. C. and Bishop R. H., Modern Control Systems (Addison Wesley, Menlo Park, California, 1998) ISBN: 978-0136024583.
46. Gautier M., Janot A. and Vandanjon P. O., “A new closed loop output error method for parameter identification of robot dynamics,“ IEEE Trans. Control Syst. Technol. 21, pp. 428444 (2013) doi: 10.1109/TCST.2012.2185697.
47. Ikonen E. and Najim K., Advanced Process, Identification and Control, Automation and Control Engineering, 1st ed. (CRC Press, 2001) ISBN: 978-0824706487.
48. Ioannou P. A. and Sun J., “Robust Adaptive Control,“ In: Dover Books and Electrical Engineering, 1st ed. (Dover Publications, 2012) ISBN: 978-0486498171.
49. Moreno-Valenzuela J. and Aguilar-Avelar C., Motion Control of Underactuated Mechanical Systems (Springer Science Business Media, in press to be published, 2017).
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

  • ISSN: 0263-5747
  • EISSN: 1469-8668
  • URL: /core/journals/robotica
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Full text views

Total number of HTML views: 0
Total number of PDF views: 114 *
Loading metrics...

Abstract views

Total abstract views: 398 *
Loading metrics...

* Views captured on Cambridge Core between 29th May 2017 - 25th November 2017. This data will be updated every 24 hours.