Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-16T05:22:29.187Z Has data issue: false hasContentIssue false
  • English
  • Français

Failure detection and isolation in robotic manipulators using joint torque sensors

Published online by Cambridge University Press:  24 June 2009

Mehrzad Namvar  and
Farhad Aghili
Mehrzad Namvar*
Affiliation:
Department of Electrical Engineering, Sharif University of Technology, P.O. Box 11155-8639, Tehran, Iran
Farhad Aghili
Affiliation:
Canadian Space Agency, St. Hubert, Quebec, CanadaJ3Y 8Y9
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Reliability of any model-based failure detection and isolation (FDI) method depends on the amount of uncertainty in a system model. Recently, it has been shown that the use of joint torque sensing results in a simplified manipulator model that excludes hardly identifiable link dynamics and other nonlinearities such as friction, backlash, and flexibilities. In this paper, we show that the application of the simplified model in a fault detection algorithm increases reliability of fault monitoring system against modeling uncertainty. The proposed FDI filter is based on a smooth velocity observer of degree 2n where n stands for the number of manipulator joints. No velocity measurement and assumptions on smoothness of faults are used in the fault detection process. The paper focuses on actuator faults and investigates the effect of torque sensor noise on threshold selection. The FDI filter is further improved to become robust against an unknown bias in torque sensor reading. The effect of position sensor noise together with position sensor faults are also investigated. Simulation example on a 6-degrees of freedom manipulator is carried out to illustrate the performance of the proposed FDI method.

Keywords

Robotic self-diagnosisFault detection and IsolationJoint torque sensingControl of robotic systemsActuator and sensor faults
Type
Article
Information
Robotica , Volume 28 , Issue 4 , July 2010 , pp. 549 - 561
Copyright
Copyright © Cambridge University Press 2009

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.Frank, P., “Fault diagnosis in dynamic systems using analytical and knowledge based redundancy: A survey,” Automatica 26, 459–474 (1990).CrossRefGoogle Scholar
2.Persis, C. and Isidori, A., “A geometric approach to nonlinear fault detection and isolation,” IEEE Trans. Autom. Control 46 (6), 853–865 (2001).CrossRefGoogle Scholar
3.Hammouri, H., Kinnaert, M. and Yaaghoubi, E. E., “Observer based approach to fault detection and isolation for nonlinear systems,” IEEE Trans. Autom. Control 44, 1879–1884 (1999).CrossRefGoogle Scholar
4.Massoumnia, M., “A geometric approach to the synthesis of failure detection filters,” IEEE Trans. Autom. Control 31, 839–846 (1986).CrossRefGoogle Scholar
5.Mattone, R. and De-Luca, A., “Relaxed fault detection and isolation: An application to a nonlinear case study,” Automatica 42, 109–116 (2006).CrossRefGoogle Scholar
6.Leuschen, M., Walker, I. and Cavallaro, J., “Fault residual generation via nonlinear analytical redundancy,” IEEE Trans. Control Syst. Technol. 13 (3), 452–458 (2005).CrossRefGoogle Scholar
7.Filaretov, V., Vukobratovicb, M. and Zhirabok, A., “Observer-based fault diagnosis in manipulation robots,” Mechatronics 9, 929–939 (1999).CrossRefGoogle Scholar
8.Schneider, H. and Frank, P. M., “Observer based supervision and fault detection in robots using nonlinear and fuzzy logic residual evaluation,” IEEE Trans. Control Syst. Technol. 4 (3), 274–282 (1996).CrossRefGoogle Scholar
9.Verma, V. and Simmonsb, R., “Scalable robot fault detection and identification,” Robot. Auton. Syst. 54, 184–191 (2006).CrossRefGoogle Scholar
10.Dixon, W., Walker, I., Dawson, D. and Hartranft, J., “Fault detection for robot manipulators with parametric uncertainty: A prediction error based approach,” IEEE Trans. Robot. Autom. 16 (6), 689–699 (2000).CrossRefGoogle Scholar
11.McIntyre, M., Dixon, W., Dawson, D. and Walker, I., “Fault identification for robot manipulators,” IEEE Trans. Robot. 21 (5), 1028–1034 (2005).CrossRefGoogle Scholar
12.Kosuge, K., Takeuchi, H. and Furuta, K., “Motion control of a robot arm using joint torque sensing,” IEEE Trans. Robot. Autom. 6 (2), 258–263 (1990).CrossRefGoogle Scholar
13.Hashimoto, M., Kiyosawa, Y. and Paul, R., “A torque sensing technique for robots with harmonic drives,” IEEE Trans. Robot. Autom. 9 (1), 108–116 (1993).CrossRefGoogle Scholar
14.Aghili, F. and Namvar, M., “Adaptive control of manipulators using un-calibrated joint-torque sensing,” IEEE Trans. Robot. 22 (4), 854–860 (2006).CrossRefGoogle Scholar
15.Spong, M., Hutchinson, S. and Vidyasagar, M., Robot Modeling and Control (John Wiley and Sons, USA, 2006).Google Scholar
16.Khalil, H. K., Nonlinear Systems (Prentice Hall, New Jersey, USA, 1996).Google Scholar