Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-25T09:06:49.464Z Has data issue: false hasContentIssue false
  • English
  • Français

Active control of flexible one-link manipulators using wavelet networks

Published online by Cambridge University Press:  07 June 2013

V. I. Gervini ,
E. M. Hemerly  and
S. C. P. Gomes
V. I. Gervini
Affiliation:
Applied Mathematics and Control Laboratory, Federal University of Rio Grande (FURG), Av. Itália Km 8, 96201-900, Rio Grande, RS, Brazil
E. M. Hemerly
Affiliation:
Technological Institute of Aeronautics (ITA), Electronics Division Praça Marechal Eduardo Gomes 50, 12228-900, São José dos Campos, SP, Brazil
S. C. P. Gomes*
Affiliation:
Applied Mathematics and Control Laboratory, Federal University of Rio Grande (FURG), Av. Itália Km 8, 96201-900, Rio Grande, RS, Brazil
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The design of control laws for flexible manipulators is known to be a challenging problem, when using a conventional actuator, i.e., a motor with gear. This is due to the friction of the nonlinear actuator, which causes torque dead zone and stick-slip behavior, thereby hampering the good performance of the control system. The torque needed to attenuate the vibrations, although calculated by the control law, is consumed by the friction inside the actuator, rendering it ineffective to the flexible structure control. Nonlinear friction varies with different operational conditions of the actuator and a friction compensation mechanism based on these models cannot always keep a good performance. This study proposes a new control strategy using wavelet network to friction compensation. Experimental results obtained with a flexible manipulator attest to the good performance of the proposed control law.

Keywords

Flexible robotsWavelet networksFrictionControlModeling
Type
Articles
Information
Robotica , Volume 31 , Issue 8 , December 2013 , pp. 1275 - 1283
Copyright
Copyright © Cambridge University Press 2013 

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.Armstrong, B. S. R., Dynamics for Robot Control: Friction Modeling and Ensuring Excitation During Parameter IdentificationPh.D. Dissertation (California, USA: Stanford University, 1988).Google Scholar
2.Gervini, V. I., Gomes, S. C. P. and Rosa, V. S., “A new robotic drive joint friction compensation mechanism using neural networks,” J. Braz. Soc. Mech. Sci. Eng. (ABCM) 25 (2), 129–139 (2003).Google Scholar
3.Canudas, C.de Wit, H. Olsson and Aströn, K., “A new model for control of systems with friction,” IEEE Trans. Autom. Control 40 (3), 419–425 (1995).CrossRefGoogle Scholar
4.Gomes, S. C. P. and Chrétien, J. P., “Dynamic Modeling and Friction Compensated Control of a Robot Manipulator Joint,” Proceedings of IEEE Robotics and Automation Conference, Nice, France (May 1992).Google Scholar
5.Gomes, S. C. P. and Rosa, V. S., “A New Approach to Compensate Friction in Robotic Actuators,” Proceedings of IEEE International Conference on Robotics and Automation, Taipei, Taiwan (Sep. 2003), vol. 1, pp. 622–627.Google Scholar
6.Bona, B. and Indri, M., “Friction Compensation in Robotics: An Overview,” Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005, Seville, Spain (Dec. 12–15, 2005).Google Scholar
7.Bittencourt, A. C. and Gunnarsson, S., “Static friction in a robot joint—modeling and identification of load and temperature effects,” J. Dyn. Syst. Meas. Control 134 (5), 051013 (2012).CrossRefGoogle Scholar
8.Schmitz, E., Experiments on the End-Point Control of a Very Flexible One-Link ManipulatorPh.D. Dissertation (California, USA: Stanford University, 1985).Google Scholar
9.Cannon, R. H. and Rosenthal, D. E., “Experiments in control of flexible structures with noncolocated sensors and actuators,” J. Guid. Control 7 (5), 546–553 (1984).CrossRefGoogle Scholar
10.Mahmood, I. A., Reza Moheimani, S. O. and Bhikkaji, B., “Positioning of a flexible manipulator using resonant control,” IEEE/ASME Trans. Mechatronics 13 (2), 180–186 (2008).CrossRefGoogle Scholar
11.Gomes, S. C. P., Rosa, V. S. and Albertini, B. C., “Active control to flexible manipulators,” IEEE/ASME Trans. Mechatronics 11 (1), 75–83 (2006).CrossRefGoogle Scholar
12.Qiu, Z., “Acceleration Sensor Based Vibration Control for Flexible Robot by Using PPF Algorithm,” Proceedings of the IEEE International Conference on Control and Automation, Guangzhou, China (May.–Jun. 2007) pp. 1335–1339.Google Scholar
13.Zhang, Q. and Benveniste, A., “Wavelet network,” IEEE Trans. Neural Netw. 3, 889–898 (1992).CrossRefGoogle Scholar
14.Sun, W., Wang, Y. and Mao, J., “Using Wavelet Network for Identifying the Model of Robot Manipulator,” Proceedings of the 4th World Congress on Intelligent Control and Automation, Shanghai, China, vol. 2, (Jun. 2002) pp. 1634–1638.Google Scholar
15.Naerum, E., Cornellà, J. and Elle, O. J., “Wavelet Networks for Estimation of Coupled Friction in Robotic Manipulator,” Proceedings of the IEEE International Conference on Robotic and Automation, Pasadena, CA, USA (May 2008).Google Scholar
16.Cheng, D. X. P., “End-Point Control of a Flexible Structure Mounted Manipulator Based on Wavelet Basis Function Networks,” Proceedings of the IEEE IROS Work Shop on Robot Vision for Space Application, Alberta, Canada (Aug. 2005) pp. 29–34.Google Scholar
17.Gu, D. and Hu, H., “Wavelet Neural Network Based Predictive Control for Mobile Robots,” Proceedings of the IEEE International Conference on System, Man and Cybernetics, Nashville, TN (Oct. 2000), vol. 5, pp. 3544–3549.Google Scholar
18.Sung, J. Y., Jin, B. P. and Yoon, H. C., “Adaptive Dynamic Surface Control of Flexible-Joint Robots Using Self-Recurrent Wavelet Neural Networks,” Proceedings of the IEEE International Conference on System, Man and Cybernetics, Taipei, Taiwan (Dec. 2006) pp. 1342–1355.Google Scholar
19.Houglin, D. and Nair, S. S., “Identification of Friction at Low Velocities Using Wavelet Basis Function Network,” American Control Conference, Philadelphia, PA (Jun. 1998), vol. 3, pp. 1918–1922.Google Scholar
20.Ming, C., Qing-xuan, J. and Han-xu, S., “Robust tracking control of flexible joint with nonlinear friction and uncertainties using wavelet neural networks,” Intell. Comput. Technol. Autom. 1, 878–883 (2009).Google Scholar
21.Machado, C. C., Pereira, A. E., Gomes, S. C. P. and De Bortoli, A. E., “A new algorithm to flexible manipulator dynamic modeling,” Control Autom Mag. Braz. Soc. Autom. (SBA) 13 (2), 134–140 (2002).Google Scholar
22.Miu, D. K., “Physical interpretation of transfer function zeros for simple control systems with mechanical flexibilities,” J. Dyn. Syst. Meas. Control 113 (3), 419–424 (1991).CrossRefGoogle Scholar
23.Galvão, R. K. H., Yoneyama, T. and Rabello, T. N., “Signal representation by adaptive biased wavelet expansions,” Digit. Signal Process. 9 (4), 255–240 (1999).Google Scholar
24.Daubechies, I., Ten Lectures on Wavelets (Philadelphia, PA: SIAM, 1992).CrossRefGoogle Scholar
25.Yesildirek, A., Vandegrift, M. W. and Lewis, F. L., “A Neural Network Controller for Flexible-Link Robots,” Proceedings of the IEEE International Symposium on Intelligent Control, Columbus, OH (Aug. 1994) pp. 63–68.Google Scholar
26.Lewis, F. L., Abdallah, C. T. and Dawson, D. M., Control of Robot Manipulators (New York: Macmillan, 1993).Google Scholar
27.Narendra, K. S. and Annaswamy, A. M., “A new adaptive law for robust adaptation without persistent excitation,” IEEE Trans. Autom. Control 32 (2), 134–145 (1987).CrossRefGoogle Scholar