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Article contents

Experimental parameter identification of flexible joint robot manipulators

Published online by Cambridge University Press:  29 May 2017

Roger Miranda-Colorado
Affiliation:
CONACyT-Instituto Politécnico Nacional-CITEDI, Av. Instituto Politécnico Nacional No. 1310, Nueva Tijuana, Tijuana, Baja California, 22435, México. email: rmirandaco@gmail.com
Javier Moreno-Valenzuela*
Affiliation:
Instituto Politécnico Nacional-CITEDI, Av. Instituto Politécnico Nacional No. 1310, Nueva Tijuana, Tijuana, Baja California, 22435, México
*
*Corresponding author. E-mail: moreno@citedi.mx

Summary

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.

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Articles
Copyright
Copyright © Cambridge University Press 2017 

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References

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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
13. Khorasani, K., “Adaptive control of flexible-joint robots,“ IEEE Trans. Robot. Autom. 8 (2), 250267 (1992) doi: 10.1109/70.134278.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
19. Leahy, M. B. and Saridis, G. N., “Compensation of industrial manipulator dynamics,“ Int. J. Robot. Res. 8, 7384 (1989) doi: 10.1177/027836499000900406.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
32. Canudas, C., Siciliano, B. and Bastin, G., Theory of Robot Control (Springer Verlag, London, 1996) doi: 10.1007/978-1-4471-1501-4.Google Scholar
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.CrossRefGoogle Scholar
34. Craig, J. J., Introduction to Robotics: Mechanics and Control, 3rd ed. (Prentice Hall, 2004) ISBN: 978-0201543612.Google Scholar
35. Miranda, R., Cinemática y Dinámica de Robots Manipuladores (Alfaomega, 2016) ISBN: 978-607-622-048-1.Google Scholar
36. Kelly, R., Santibañez, V. and Loría, A., Control of Robot Manipulators in Joint Space (Springer Verlag, London, 2005) doi: 10.1007/b135572.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
39. Khalil, W. and Dombre, E., Modeling, identification and Control of Robots, 3rd ed. (Taylor & Francis, Bristol, 2002) ISBN: 978-1-903996-66-9.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
45. Dorf, R. C. and Bishop, R. H., Modern Control Systems (Addison Wesley, Menlo Park, California, 1998) ISBN: 978-0136024583.Google Scholar
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.CrossRefGoogle Scholar
47. Ikonen, E. and Najim, K., Advanced Process, Identification and Control, Automation and Control Engineering, 1st ed. (CRC Press, 2001) ISBN: 978-0824706487.Google Scholar
48. Ioannou, P. A. and Sun, J., “Robust Adaptive Control,“ In: Dover Books and Electrical Engineering, 1st ed. (Dover Publications, 2012) ISBN: 978-0486498171.Google Scholar
49. Moreno-Valenzuela, J. and Aguilar-Avelar, C., Motion Control of Underactuated Mechanical Systems (Springer Science Business Media, in press to be published, 2017).Google Scholar
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