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An adaptive actuator failure compensation scheme for a cooperative manipulator system

Published online by Cambridge University Press:  07 October 2014

Thummaros Rugthum  and
Gang Tao
Thummaros Rugthum
Affiliation:
Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904, USA
Gang Tao*
Affiliation:
Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904, USA
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper develops a new adaptive actuator failure compensation algorithm for the control of a cooperative robotic system subject to uncertain actuator failures. The benchmark robotic system has two manipulators to balance a rigid platform, and the right-side manipulator contains one actuator and the left-side manipulator has two actuators (one of which may fail during system operation, but it is uncertain how much, when and which actuator may fail). The developed adaptive actuator failure compensation scheme, based on adaptive integration of three individual failure compensators and direct adaptation of controller parameters, is capable of ensuring desired closed-loop stability and asymptotic output tracking, despite the failure uncertainties. Such an adaptive actuator failure compensation method is extended to control of a general cooperative robotic system. Simulation results are shown to verify the desired adaptive failure compensation control performance.

Keywords

AutomationControl of robotic systemsMulti-robot systemsRedundant manipulatorsRobot dynamics
Type
Articles
Information
Robotica , Volume 34 , Issue 7 , July 2016 , pp. 1529 - 1552
Copyright
Copyright © Cambridge University Press 2014 

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References

1.Dede, M. and Tosunoglu, S., “Fault-tolerant teleoperation systems design,” Indust. Robot Int. J. 33 (5), 365–372 (2006).Google Scholar
2.Ohno, K., Kawatsuma, S., Okada, T., Takeuchi, E., Higashi, K. and Tadokoro, S., “Robotic control vehicle for measuring radiation in Fukushima Daiichi Nuclear Power Plant,” Proceedings of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), (Nov. 2011) pp. 38–43.CrossRefGoogle Scholar
3.Jones, K., “Special weapons observation remote recon direct action system (SWORDS),” Platform Innovations and System Integration for Unmanned Air, Land and Sea Vehicles (2007) pp. 36–1 36–8.Google Scholar
4.Weinberger, S., “Armed robots still in Iraq, but grounded,” April 15, (2008). Available at http://www.wired.com/dangerroom/2008/04/armed-robots-st/.Google Scholar
5.Liu, G., “Control of robot manipulators with consideration of actuator performance degradation and failures,” Proceedings of the 2001 IEEE International Conference on Robotics and Automation, Seoul, Korea (May 21–26, 2001).Google Scholar
6.Ozkan, M., Kirlik, G., Parlaktuna, O., Yufka, A. and Yazici, A., “A multi-robot control architecture for fault-tolerant sensor-based coverage,” Int. J. Adv. Robot. Syst. 7 (1), 067–074 (2010) ISSN 1729-8806.Google Scholar
7.Al-Bayati, A. H. and Wang, H., “A robust new nonlinear fault tolerant control for robot via an observer,” Proceedings of the 8th World Congress on Intelligent Control and Automation (Jun. 21–25, 2011).Google Scholar
8.Ji, M. and Sarkar, N., “Supervisory fault adaptive control of a mobile robot and its application in sensor-fault accommodation,” IEEE Trans. Robot. 23 (1), (Feb. 2007).Google Scholar
9.Zhang, X., Polycarpou, M. M. and Parisini, T., “Fault diagnosis of a class of nonlinear uncertain systems with Lipschitz nonlinearities using adaptive estimation,” Automatica 46 (2)290–299 (2010).CrossRefGoogle Scholar
10.Diao, Y. and Passino, K. M., “Stable fault-tolerant adaptive fuzzy/neural control for turbine engine” IEEE Trans. Control Syst. Technol. 9 (3), 494–509 (2001).Google Scholar
11.Jiang, B., Gao, Z., Shi, P. and Xu, Y., “Adaptive fault-tolerant tracking control of near-space vehicle using takagi-sugeno fuzzy models,” IEEE Trans. Fuzzy Syst. 18 (5), 1000–1007 (2001).Google Scholar
12.Axenie, C. and Cernega, D., “Adaptive sliding mode controller design for mobile robot fault tolerant control. Introducing ARTEMIC,” 19th International Workshop on Robotics in Alpe-Adria-Danube Region RAAD 2010, Budapest, Hungary (Jun. 23–25, 2010).Google Scholar
13.Wu, L., Lam, J. and Parisini, T., “Sliding mode control of switched hybrid systems with time-varying delay,” Int. J. Adapt. Control Signal Process. 22 (1), 990–931 (2008).Google Scholar
14.Yao, X. L., Tao, G., Qi, R. Y. and Jiang, B., “An adaptive actuator failure compensation scheme for an attitude dynamic model of near space vehicles,” Proceedings of the 2012 American Control Conference, Montreal, Canada (Jun. 2012) pp. 368–373.Google Scholar
15.Liu, Y., Tang, X. and Tao, G., “Adaptive failure compensation control of autonomous robotic systems: Application to a precision pointing hexapod,” Proceedings of AIAA Infotech@Aerospace Conference, Arlington, VA (2005).Google Scholar
16.Jean, J. H. and Fu, L. C., “An adaptive control scheme for coordinated multimanipulator Systems,” IEEE Trans. Robot. Autom. 9 (2), (Apr. 1993).Google Scholar
17.Kececi, F., Tang, X. and Tao, G., “Adaptive actuator failure compensation for redundant manipulator,” Robotica 27 (1), (Jan. 2009).CrossRefGoogle Scholar
18.Khalil, H. K., “Nonlinear Design Tool,” In: Nonlinear System, 3rd ed. (New Jersey, Prentice Hall, 2002) pp. 589–603.Google Scholar
19.Yao, X. L., Tao, G., Qi, R. Y. and Jiang, B., “An adaptive actuator failure compensation scheme for an attitude dynamic model of near space vehicles,” Proceedings of the 2012 American Control Conference, Montreal, Canada (Jun. 2012) pp. 368–373.Google Scholar
20.Spong, M. W. and Vidyasagar, M., Robot Dynamics and Control (John Wiley & Sons, New York, 1989).Google Scholar
21.Lazarevic, M. P., “Mathematical modeling and control of redundant robotic manipulators using biological analog,” Mechanics Automatic Control and Robotics 3 (11), 2001, 285–294.Google Scholar