Fault tolerance in networked control systems under intermittent observations

Jean-Philippe Georges; Didier Theilliol; Vincent Cocquempot; Jean-Christophe Ponsart; Christophe Aubrun

International Journal of Applied Mathematics and Computer Science (2011)

  • Volume: 21, Issue: 4, page 639-648
  • ISSN: 1641-876X

Abstract

top
This paper presents an approach to fault tolerant control based on the sensor masking principle in the case of wireless networked control systems. With wireless transmission, packet losses act as sensor faults. In the presence of such faults, the faulty measurements corrupt directly the behaviour of closed-loop systems. Since the controller aims at cancelling the error between the measurement and its reference input, the real outputs will, in such a networked control system, deviate from the desired value and may drive the system to its physical limitations or even to instability. The proposed method facilitates fault compensation based on an interacting multiple model approach developed in the framework of channel errors or network congestion equivalent to multiple sensors failures. The interacting multiple model method involved in a networked control system provides simultaneously detection and isolation of on-line packet losses, and also performs a suitable state estimation. Based on particular knowledge of packet losses, sensor fault-tolerant controls are obtained by computing a new control law using fault-free estimation of the faulty element to avoid intermittent observations that might develop into failures and to minimize the effects on system performance and safety.

How to cite

top

Jean-Philippe Georges, et al. "Fault tolerance in networked control systems under intermittent observations." International Journal of Applied Mathematics and Computer Science 21.4 (2011): 639-648. <http://eudml.org/doc/208076>.

@article{Jean2011,
abstract = {This paper presents an approach to fault tolerant control based on the sensor masking principle in the case of wireless networked control systems. With wireless transmission, packet losses act as sensor faults. In the presence of such faults, the faulty measurements corrupt directly the behaviour of closed-loop systems. Since the controller aims at cancelling the error between the measurement and its reference input, the real outputs will, in such a networked control system, deviate from the desired value and may drive the system to its physical limitations or even to instability. The proposed method facilitates fault compensation based on an interacting multiple model approach developed in the framework of channel errors or network congestion equivalent to multiple sensors failures. The interacting multiple model method involved in a networked control system provides simultaneously detection and isolation of on-line packet losses, and also performs a suitable state estimation. Based on particular knowledge of packet losses, sensor fault-tolerant controls are obtained by computing a new control law using fault-free estimation of the faulty element to avoid intermittent observations that might develop into failures and to minimize the effects on system performance and safety.},
author = {Jean-Philippe Georges, Didier Theilliol, Vincent Cocquempot, Jean-Christophe Ponsart, Christophe Aubrun},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {network congestion; fault-tolerant control; fault diagnosis; networked control system; interacting multiple model},
language = {eng},
number = {4},
pages = {639-648},
title = {Fault tolerance in networked control systems under intermittent observations},
url = {http://eudml.org/doc/208076},
volume = {21},
year = {2011},
}

TY - JOUR
AU - Jean-Philippe Georges
AU - Didier Theilliol
AU - Vincent Cocquempot
AU - Jean-Christophe Ponsart
AU - Christophe Aubrun
TI - Fault tolerance in networked control systems under intermittent observations
JO - International Journal of Applied Mathematics and Computer Science
PY - 2011
VL - 21
IS - 4
SP - 639
EP - 648
AB - This paper presents an approach to fault tolerant control based on the sensor masking principle in the case of wireless networked control systems. With wireless transmission, packet losses act as sensor faults. In the presence of such faults, the faulty measurements corrupt directly the behaviour of closed-loop systems. Since the controller aims at cancelling the error between the measurement and its reference input, the real outputs will, in such a networked control system, deviate from the desired value and may drive the system to its physical limitations or even to instability. The proposed method facilitates fault compensation based on an interacting multiple model approach developed in the framework of channel errors or network congestion equivalent to multiple sensors failures. The interacting multiple model method involved in a networked control system provides simultaneously detection and isolation of on-line packet losses, and also performs a suitable state estimation. Based on particular knowledge of packet losses, sensor fault-tolerant controls are obtained by computing a new control law using fault-free estimation of the faulty element to avoid intermittent observations that might develop into failures and to minimize the effects on system performance and safety.
LA - eng
KW - network congestion; fault-tolerant control; fault diagnosis; networked control system; interacting multiple model
UR - http://eudml.org/doc/208076
ER -

References

top
  1. Andersson, M., Henriksson, D. and Cervin, A. (2007). Truetime 1.5: Reference Manual, Department of Automatic Control, Lund Institute of Technology, Lund. 
  2. Blanke, M., Frei, C., Kraus, F., Patton, R. and Staroswiecki, M. (2000). What is fault-tolerant control?, IFAC Safeprocess'2000, Symposium Budapest, Hungary, Vol. 1, pp. 40-51. 
  3. CENELEC (1996). Fieldbus. Vol. 1: P-net, Vol. 2: Profibus, Vol. 3: Worlfip, European Standard EN50170. 
  4. Cuzzocrea, C., Dandache, A., Georges, J.-P., Jean, P., Monteiro, F., Theilliol, D. and Yamé, J. (2008). Analysis of wireless transmissions QoS relatively to the dependability of a networked control system, 23rd IAR Workshop on Advanced Control and Diagnosis, Coventry, UK, pp. 210-215. 
  5. Decotignie, J.-D. (2002). Wireless fieldbusses-A survey of issues and solutions, 15th IFAC Triennal World Congress, Barcelona, Spain. 
  6. De Pellegrini, F., Miorandi, D., Vitturi, S. and Zanella, A. (2006). On the use of wireless networks at low level of factory automation systems, IEEE Transactions on Industrial Informatics 2(2): 129-143. 
  7. He, X., Wang, Z. and Zhou, D. (2009). Robust fault detection for networked systems with communication delay and data missing, Automatica 45(11): 2634-2639. Zbl1180.93101
  8. Henk, A., Bloom, O. and Bar-Shalom, Y. (1988). The interacting multiple model algorithm for systems with Markovian switching coefficients, IEEE Transactions on Automatic Control 33(8): 780-783. Zbl0649.93065
  9. IEEE Computer Society (2003). IEEE standard for information technology, Telecommunications and information exchange between systems, Local and metropolitan area networks, Specific requirements, Part 15.4: Wireless medium access control (mac) and physical layer (phy) specifications for low-rate wireless personal area networks (lrwpans), IEEE Std 802.15.4-2003. 
  10. Mao, Z., Jiang, B. and Shi, P. (2009). Fault detection for a class of nonlinear networked control systems, International Journal of Adaptive Control and Signal Processing 24(7): 610-622. Zbl1200.93085
  11. Noura, H., Sauter, D., Hamelin, F. and Theilliol, D. (2000). Fault-tolerant control in dynamic systems: Application to a winding machine, IEEE Control Systems Magazine 20(1): 33-49. 
  12. Patan, M. and Uciński, D. (2008). Configuring a sensor network for fault detection in distributed parameter systems, International Journal of Applied Mathematics and Computer Science 18(4): 513-524, DOI: 10.2478/v10006-0080045-4. Zbl1155.93426
  13. Patton, R. (1997). Fault-tolerant control: The 1997 situation, IFAC Symposium Safeprocess'97, Kingston Upon Hull, UK, Vol. 2, pp. 1033-1055. 
  14. Sinopoli, B., Schenato, L., Franceschetti, M., Poolla, K., Jordan, M. and Sastry, S. (2004). Kalman filtering with intermittent observations, IEEE Transactions on Automatic Control 49(9): 1453-1464. 
  15. Theilliol, D., Rodrigues, M. and Ponsart, J. (2008). Fault diagnosis and accommodation design for nonlinear systems described by interpolated LTI models, 16th Mediterranean Conference on Control and Automation, Ajaccio, France, pp. 267-273. 
  16. Tipsuwan, Y. and Chow, M.-Y. (2003). Control methodologies in networked control systems, Control Engineering Practice 11(10): 1099-1111. 
  17. Wang, Y., Ding, S., Ye, H., Wei, L., Zhang, P. and Wang, G. (2009). Fault detection of networked control systems with packet based periodic communication, International Journal of Adaptive Control and Signal Processing 23(8): 682-698. Zbl1193.93103
  18. Willig, A., Kubisch, M., Hoene, C. and Wolisz, A. (2002). Measurements of a wireless link in an industrial environment using an IEEE 802.11-compliant physical layer, IEEE Transactions on Industrial Electronics 49(6): 1265-1282. 
  19. Willig, A., Matheus, K. and Wolisz, A. (2005). Wireless technology in industrial networks, Proceedings of the IEEE 93(6): 1130-1151. 
  20. Wu, E., Thavamani, S., Zhang, Y. and Blanke, M. (2006). Sensor fault masking of a ship propulsion, Control Engineering Practice 14(11): 1337-1345. 
  21. Xiong, J. and Lam, J. (2007). Stabilization of linear systems over networks with bounded packet loss, Automatica 43(1): 80-87. Zbl1140.93383
  22. Zhang, W., Branicky, S. and Phillips, S. (2001). Stability of networked control systems, IEEE Control Systems Magazine 21(1): 84-89. 
  23. Zhang, Y. and Jiang, J. (2002). Active fault-tolerant control system against partial actuator failures, IEE Proceedings: Control Theory and Applications 149(1): 95-104. 
  24. Zhang, Y. and Jiang, J. (2008). Bibliographical review on reconfigurable fault-tolerant control systems, Annual Reviews in Control 32(2): 229-252. 
  25. Zhang, Y. and Li, X. R. (1998). Detection and diagnosis of sensors and actuators failures using IMM estimator, IEEE Transactions on Aerospace and Electronic Systems 34(4): 1293-1313. 
  26. Zhao, Y., Lam, Y. and Gao, H. (2009). Fault detection for fuzzy systems with intermittent measurements, International Journal of Adaptive Control and Signal Processing 17(2): 298-410. 

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.