Hostname: page-component-8448b6f56d-xtgtn Total loading time: 0 Render date: 2024-04-24T15:31:43.449Z Has data issue: false hasContentIssue false

Experimental backlash study in mechanical manipulators

Published online by Cambridge University Press:  04 March 2010

Miguel F. M. Lima*
Affiliation:
Department of Electrical Engineering, Superior School of Technology, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
J. A. Tenreiro Machado
Affiliation:
Department of Electrical Engineering, Institute of Engineering, Polytechnic Institute of Porto, 4200-072 Porto, Portugal
Manuel Crisóstomo
Affiliation:
Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, Polo II, 3030-290 Coimbra, Portugal
*
*Corresponding author. E-mail: lima@mail.estv.ipv.pt

Summary

The behavior of mechanical manipulators with backlash is analyzed. In order to acquire and study the signals an experimental setup is implemented. The signal processing capabilities of the wavelets are used for de-noising the experimental signals and the energy of the obtained components is analyzed. To evaluate the backlash effect upon the robotic system, it is proposed an index based on the pseudo phase plane representation. Several tests are developed that demonstrate the coherence of the results.

Type
Article
Copyright
Copyright © Cambridge University Press 2010

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

1.Dagalakis, N. G. and Myers, D. R., “Adjustment of robot joint gear backlash using the robot joint test excitation technique,” Int. J. Robot. Res. 4 (2), 6579 (1985).Google Scholar
2.Stein, J. L. and Wang, C.-H., “Automatic detection of clearance in mechanical systems: Theory and simulation,” Proc. Am. Control Conf. 3, 17371745 (1995).Google Scholar
3.Sarkar, N., Ellis, R. E. and Moore, T. N., “Backlash detection in geared mechanisms: modeling, simulation, and experimentation,” Mech. Syst. Signal Process. 11 (3), 391408 (1997).CrossRefGoogle Scholar
4.Hovland, G., Hanssen, S., Moberg, S., Brogårdh, T., Gunnarsson, S. and Isaksson, M., “Nonlinear Identification of Backlash in Robot Transmissions,” Proceedings of the 33rd International Symposium on Robotics (ISR 2002), Stockholm, Sweden (Oct. 7–11, 2002). Paper in CD-Rom proceedings.Google Scholar
5.Merzouki, R., Davila, J. A., Cadiou, J. C. and Fridman, L., “Backlash Phenomenon Observation and Identification,” Proceedings of American Control Conference, Minneapolis, MN (2006) pp. 33223327.Google Scholar
6.Trendafilova, I. and Brussel, H. Van., “Non-linear dynamics tools for the motion analysis and condition monitoring of robot joints,” Mech. Syst. Signal Process. 15 (6), 11411164 (Nov. 2001).Google Scholar
7.Azenha, A. and Machado, J. A. T., “Variable Structure Control of Robots with Nonlinear Friction and Backlash at the Joints,” Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, USA (1996) pp. 366371.Google Scholar
8.Nordin, M. and Gutman, P.-O., “Controlling mechanical systems with backlash – A survey,” Automatica 38, 16331649 (2002).CrossRefGoogle Scholar
9.Ma, C. and Hori, Y., “The Application of Fractional Order Control to Backlash Vibration Suppression,” Proceedings of American Control Conference, Boston, MA (2004) pp. 29012906.Google Scholar
10.Nordin, M. and Gutman, P.-O., “Non-Linear Speed Control of Elastic Systems with Backlash,” Proceedings. 39th IEEE Conf. on Decision and Control, Sydney, Australia (Dec. 2000) pp. 40604065.Google Scholar
11.David, R. Seidl, Lam, Sui-Lun, Putman, Jerry A. and Lorenz, Robert D., “Neural network compensation of gear backlash hysteresis in position-controlled mechanisms,” IEEE Trans. Ind. Appl. 31, 14751483 (6 Nov./Dec., 1995).Google Scholar
12.Su, C.-Y., Oya, M. and Hong, H., “Stable adaptive fuzzy control of nonlinear systems preceede by unknown backlash-like hysteresis,” IEEE Trans. Fuzzy Syst. 11 (1), 18 (Feb. 2003).Google Scholar
13.Shi, Z., Zhong, Y., Xu, W. and Zhao, M., “Decentralized Robust Control of Uncertain Robots with Backlash and Flexibility at Joints,” IEEE/International Conference on Intelligent Robots and Systems, Beijing, China (2006) pp. 45214526.Google Scholar
14.Xu, K. and Simaan, N., “Actuation Compensation for Flexible Surgical Snake-like Robots with Redundant Remote Actuation,” IEEE Conf. on Robotics and Automation, Orlando, FL (2006) pp. 41484154.Google Scholar
15.Park, E. J. and Mills, J. K., “Static shape and vibration control of flexible payloads with applications to robotic assembly,” IEEE/ASME Trans. Mechatronics 6, 10, 675687 (2005).CrossRefGoogle Scholar
16.Mouri, T., Kawasaki, H. and Umebayashi, K., “Developments of new anthropomorphic robot hand and its master slave system,” IEEE IROS 3225–3230 (2005).Google Scholar
17.Lima, M. F. M., Machado, J. A. T. and Crisóstomo, M., “Experimental set-up for vibration and impact analysis in robotics,” WSEAS Trans. Syst. 4 (5), 569576 (May, 2005).Google Scholar
18.Mallat, S., A Wavelet Tour of Signal Processing, 2nd ed. (Academic Press, London, UK, 1999).Google Scholar
19.Mallat, S., “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11 (7), 674693 (1989).Google Scholar
20.Torrence, C. and Compo, G. P., “A practical guide to wavelet analysis,” Bull. Am. Meteorol. Soc. 79 (1), 6178 (1998).2.0.CO;2>CrossRefGoogle Scholar
21.Feeny, B. F. and Lin, G., “Fractional derivatives applied to phase-space reconstructions, special issue on fractional calculus,” Nonlinear Dyn. 38 (1–4), 8599 (2004).CrossRefGoogle Scholar
22.Henry, D. I. Abarbanel, Brown, Reggie, Sidorowich, John J. and Tsimring, Lev Sh., “The analysis of observed chaotic data in physical systems,” Rev. Mod. Phys. 65 (4), 13311392 (1993).Google Scholar
23.Shannon, C. E., “A mathematical theory of communication,” Bell Syst. Tech. J. 27, 379423; 623–656 (July, Oct. 1948).CrossRefGoogle Scholar