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Robust detection and isolation of failures in satellite attitude sensors and gyro

Published online by Cambridge University Press:  12 January 2012

Bahar Ahmadi  and
Mehrzad Namvar
Bahar Ahmadi
Affiliation:
Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
Mehrzad Namvar*
Affiliation:
Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
*
*Corresponding author. E-mail: namvar@sharif.ir
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Summary

Reliability of a satellite attitude control system depends on accurate detection of failures in its sensors. This paper presents an observer for robust detection and isolation of a class of failures in satellite attitude sensors. The proposed observer uses measurement of a three-axis gyro together with only one attitude sensor, and generates a residual signal which is sensitive to faults and is simultaneously robust against disturbance and noise. A nonlinear model of satellite kinematics is considered for design of the observer. The structure of the observer is in the form of a delayed continuous-time differential equation ensuring its robustness properties. A realistic simulation is provided to illustrate the performance of the proposed observer in the face of the faults occurring in a magnetometer, as the attitude sensor, and also the faults occurring in the gyro.

Keywords

Robotic self-diagnosis and self-repairNavigationPose estimation and registrationMobile robotsAutomation
Type
Articles
Information
Robotica , Volume 30 , Issue 7 , December 2012 , pp. 1157 - 1166
Copyright
Copyright © Cambridge University Press 2012

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References

1. Frank, P. M., “Fault dignosis in dynanmic systems using analytical and knowledge based redundancy, a survey and some new results,” Automatica 26 (3), 459–474 (1990).CrossRefGoogle Scholar
2. Patton, R., Frank, P. and Clark, R., Fault Diagnosis in Dynamic Systems: Theory and Applications (Prentice-Hall, Englewood Cliffs, NJ, 1989).Google Scholar
3. Casavola, A., Famularo, D. and Franz, G., “A robust deconvolution scheme for fault detection and isolation of uncertain linear systems: An LMI approach,” Automatica 41, 1463–1472 (2005).CrossRefGoogle Scholar
4. Zhong, M., Ding, S. X., Lam, J. and Wang, H., “An LMI approach to design robust fault detection filter for uncertain LTI systems,” Automatica 39, 543–550 (2003).CrossRefGoogle Scholar
5. Liu, J., Wang, J. L. and Yang, G. H., “An LMI approach to minimum sensitivity analysis with application to fault detection,” Automatica 41, 1995–2004 (2005).CrossRefGoogle Scholar
6. Pertew, A. M., Marquez, H. J. and Zhao, Q., “LMI-based sensor fault diagnosis for nonlinear Lipschitz systems,” Automatica 43, 1464–1469 (2007).CrossRefGoogle Scholar
7. Tan, C. P. and Edwards, C., “Sliding mode observers for detection and reconstruction of sensor faults,” Automatica 38, 1815–1821 (2002).CrossRefGoogle Scholar
8. Li, X. and Zhou, K., “A time domain approach to robust fault detection of linear time-varying systems,” Automatica 45, 94–102 (2009).CrossRefGoogle Scholar
9. Yan, X. G. and Edwards, C., “Fault Reconstruction/Estimation Using a Sliding Mode Observer,” Proc. of the IEEE Conference on Decision and Control, San Diego, USA (2006) pp. 5573–5578.CrossRefGoogle Scholar
10. Narasimhan, S., Vachhani, P. and Rengaswamy, R., “New nonlinear residual feedback observer for fault diagnosis in nonlinear systems,” Automatica 44, 2222–2229 (2008).CrossRefGoogle Scholar
11. Zhang, K., Hu, S. and Jiang, B., “Sliding mode integral observers for sensor faults detection and isolation in nonlinear systems,” Proc. of the IEEE International Conference on Control and Automation, Singapore, Malaysia (2007) pp. 147–151.Google Scholar
12. Xu, A. and Zhang, Q., “Nonlinear system fault diagnosis based on adaptive estimation,” Automatica 40, 1181–1193 (2004).CrossRefGoogle Scholar
13. Yan, X. G. and Edwards, C., “Adaptive sliding-mode-observer-based fault reconstruction for nonlinear systems with parametric uncertainties,” IEEE Trans. Ind. Electron. 55 (11), 4029–4036 (2008).Google Scholar
14. Namvar, M. and Aghili, F., “Failure detection and isolation in robotic manipulators using joint torque sensors,” Robotica 28, 549–561 (2009).CrossRefGoogle Scholar
15. Pirmoradi, F. N., Sassani, F. and de Silva, C. W., “Fault detection and diagnosis in a spacecraft attitude determination system,” Acta Astronaut. 65, 710–729 (2009).CrossRefGoogle Scholar
16. Okatan, A., Hajiyev, C. and Hajiyeva, U., “Kalman filter innovation sequence based fault detection in LEO satellite attitude determination and control system,” Recent Adv. Space Technol. 411–416 (2007).CrossRefGoogle Scholar
17. Anderson, B. D. O. and Moore, J. B., Optimal Filtering (Prentice-Hall, Englewood Cliffs, NJ, 1979).Google Scholar
18. Venkateswaran, N., Siva, M. S. and Goel, P. S., “Analytical redundancy based fault detection of gyroscopes in spacecraft applications,” Acta Astronaut. 50 (9), 535–545 (2002).CrossRefGoogle Scholar
19. Gao, Z., Jiang, B., Shi, P. and Cheng, Y., “Sensor fault estimation and compensation for microsatellite attitude control systems,” Int. J. Control Autom. Syst. 8 (2), 228–237 (2010).CrossRefGoogle Scholar
20. Wu, L., Zhang, Y. and Li, H., “Research on fault detection for satellite attitude control systems based on sliding mode observers,” Proc. of the IEEE International Conference on Mechatronics and Automation, Changchun, Jilin, China (2009) pp. 4408–4413.Google Scholar
21. Xiong, K., Chan, C. W. and Zhang, H. Y., “Detection of satellite attitude sensor faults using the UKF,” IEEE Trans. Aeorsp. Electron. Syst. 43 (2), 480–491 (2007).CrossRefGoogle Scholar
22. Spong, M., Hutchinson, S. and Vidyasagar, M., Robot Modeling and Control. (John Wiley and Sons, Hoboken, NJ, 2006).Google Scholar
23. Richard, J. P., “Time-delay systems: An overview of some recent advances and open problems,” Automatica 39, 1667–1694 (2003).CrossRefGoogle Scholar
24. Sidi, M. J., Spacecraft Dynamics and Control. (Press Syndicate of the University of Cambridge, New York, 1997).CrossRefGoogle Scholar
25. Olsen, N., Sabaka, T. J. and Clausen, L. T., “Determination of the IGRF 2000 model,” Earth Planets Space 52, 1175–1182 (2000).CrossRefGoogle Scholar
26. Chen, W. and Saif, M., “Robust fault detection in uncertain nonlinear systems via a second order sliding mode observer,” Proceedings of the 40th IEEE Conference on Decision and Control, Orlando, Florida, USA (2001) vol. 1, pp. 573–578.Google Scholar