SMAC-FDI: A single model active fault detection and isolation system for unmanned aircraft

Guillaume J.J. Ducard

International Journal of Applied Mathematics and Computer Science (2015)

  • Volume: 25, Issue: 1, page 189-201
  • ISSN: 1641-876X

Abstract

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This article presents a single model active fault detection and isolation system (SMAC-FDI) which is designed to efficiently detect and isolate a faulty actuator in a system, such as a small (unmanned) aircraft. This FDI system is based on a single and simple aerodynamic model of an aircraft in order to generate some residuals, as soon as an actuator fault occurs. These residuals are used to trigger an active strategy based on artificial exciting signals that searches within the residuals for the signature of an actuator fault. Fault isolation is carried out through an innovative mechanism that does not use the previous residuals but the actuator control signals directly. In addition, the paper presents a complete parameter-tuning strategy for this FDI system. The novel concepts are backed-up by simulations of a small unmanned aircraft experiencing successive actuator failures. The robustness of the SMAC-FDI method is tested in the presence of model uncertainties, realistic sensor noise and wind gusts. Finally, the paper concludes with a discussion on the computational efficiency of the method and its ability to run on small microcontrollers.

How to cite

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Guillaume J.J. Ducard. "SMAC-FDI: A single model active fault detection and isolation system for unmanned aircraft." International Journal of Applied Mathematics and Computer Science 25.1 (2015): 189-201. <http://eudml.org/doc/270165>.

@article{GuillaumeJ2015,
abstract = {This article presents a single model active fault detection and isolation system (SMAC-FDI) which is designed to efficiently detect and isolate a faulty actuator in a system, such as a small (unmanned) aircraft. This FDI system is based on a single and simple aerodynamic model of an aircraft in order to generate some residuals, as soon as an actuator fault occurs. These residuals are used to trigger an active strategy based on artificial exciting signals that searches within the residuals for the signature of an actuator fault. Fault isolation is carried out through an innovative mechanism that does not use the previous residuals but the actuator control signals directly. In addition, the paper presents a complete parameter-tuning strategy for this FDI system. The novel concepts are backed-up by simulations of a small unmanned aircraft experiencing successive actuator failures. The robustness of the SMAC-FDI method is tested in the presence of model uncertainties, realistic sensor noise and wind gusts. Finally, the paper concludes with a discussion on the computational efficiency of the method and its ability to run on small microcontrollers.},
author = {Guillaume J.J. Ducard},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {fault detection and isolation; unmanned aerial vehicles; Kalman filtering; computationally efficient diagnosis system; active fault diagnosis; artificial excitation system},
language = {eng},
number = {1},
pages = {189-201},
title = {SMAC-FDI: A single model active fault detection and isolation system for unmanned aircraft},
url = {http://eudml.org/doc/270165},
volume = {25},
year = {2015},
}

TY - JOUR
AU - Guillaume J.J. Ducard
TI - SMAC-FDI: A single model active fault detection and isolation system for unmanned aircraft
JO - International Journal of Applied Mathematics and Computer Science
PY - 2015
VL - 25
IS - 1
SP - 189
EP - 201
AB - This article presents a single model active fault detection and isolation system (SMAC-FDI) which is designed to efficiently detect and isolate a faulty actuator in a system, such as a small (unmanned) aircraft. This FDI system is based on a single and simple aerodynamic model of an aircraft in order to generate some residuals, as soon as an actuator fault occurs. These residuals are used to trigger an active strategy based on artificial exciting signals that searches within the residuals for the signature of an actuator fault. Fault isolation is carried out through an innovative mechanism that does not use the previous residuals but the actuator control signals directly. In addition, the paper presents a complete parameter-tuning strategy for this FDI system. The novel concepts are backed-up by simulations of a small unmanned aircraft experiencing successive actuator failures. The robustness of the SMAC-FDI method is tested in the presence of model uncertainties, realistic sensor noise and wind gusts. Finally, the paper concludes with a discussion on the computational efficiency of the method and its ability to run on small microcontrollers.
LA - eng
KW - fault detection and isolation; unmanned aerial vehicles; Kalman filtering; computationally efficient diagnosis system; active fault diagnosis; artificial excitation system
UR - http://eudml.org/doc/270165
ER -

References

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  1. Alwi, H., Edwards, C. and Tan, C.P. (2011). Fault Detection and Fault Tolerant Control Using Sliding Modes, Springer-Verlag, London. Zbl1237.93002
  2. Athans, M. (1977). The stochastic control of the F-8c aircraft using a multiple model adaptive control (MMAC) method, Part I: Equilibrium flight, IEEE Transactions on Automatic Control 22(5): 768-780. 
  3. Azam, M., Pattipati, K., Allanach, J., Poll, S. and Petterson-Hine, A. (2005). In-flight fault detection and isolation in aircraft flight control systems, Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA, paper 1429. 
  4. Bateman, F., Noura, H. and Ouladsine, M. (2011). Active fault diagnosis and major actuator failure accommodation: Application to a UAV, in A. Balint (Ed.), Advances in Flight Control Systems, InTech, Rijeka, pp. 135-158. 
  5. Belcastro, C. and Chang, B.-C. (2002). Uncertainty modeling for robustness analysis of failure detection and accommodation systems, Proceedings of the IEEE American Control Conference, Anchorage, AK, USA, pp. 4776-4782. 
  6. Benini, B., Castaldi, P. and Simani, S. (2009). Fault Diagnosis for Aircraft System Models: An Introduction from Fault Detection to Fault Tolerance, VDM Verlag Dr. Muller, Saarbrucken. 
  7. Blanke, M., Kinnaert, M., Lunze, J. and Staroswiecki, M. (2006). Diagnosis and Fault Tolerant Control, Springer Verlag, London. Zbl1126.93004
  8. Bonfè, M., Castaldi, P., Mimmo, N. and Simani, S. (2011). Active fault tolerant control of nonlinear systems: The cart-pole example, International Journal of Applied Mathematics and Computer Science 21(3): 441-455, DOI: 10.2478/v10006-011-0033-y. Zbl1234.93054
  9. Boskovic, J.D., Redding, J. and Mehra, R.K. (2007). Robust fault tolerant flight control using a new failure parameterization, Proceedings of the IEEE American Control Conference, New York, NY, USA, pp. 5753-5758. 
  10. Brown, R.G. and Hwang, P.Y.C. (1997). Introduction to Random Signals and Applied Kalman Filtering, John Wiley & Sons, New York, NY. Zbl0868.93002
  11. Campbell, M.E., Lee, J.W., Scholte, E. and Rath Bun, D. (2007). Simulation and flight test of autonomous aircraft estimation, planning, and control algorithms, AIAA Journal of Guidance, Control, and Dynamics 30(6): 1597-1609. 
  12. Campbell, S.L. and Nikoukhah, R. (2004). Auxiliary Signal Design for Failure Detection, Princeton University Press, Princeton, NJ. Zbl1055.94036
  13. Castaldi, P., Geri, W., Bonfè, M., Simani, S. and Benini, M. (2010). Design of residual generators and adaptive filters for the FDI of aircraft model sensors, Control Engineering Practice 18(5): 449-495, DOI:10.1016/j.conengprac.2008.11.006. Zbl1229.93063
  14. Chen, J. and Patton, R.J. (1999). Robust Model-Based Diagnosis for Dynamic Systems, Kluwer Academic, Boston, MA. Zbl0920.93001
  15. Ducard, G. (2009). Fault-tolerant Flight Control and Guidance Systems: Practical Methods for Small Unmanned Aerial Vehicles, Advances in Industrial Control, Springer-Verlag, London. Zbl1162.93001
  16. Ducard, G. (2013). The SMAC fault detection and isolation scheme: Discussions, improvements, and application to a UAV, Proceedings of the IEEE 2013 Conference on Control and Fault Tolerant Systems (SysTol'13), Nice, France, pp. 480-485. 
  17. Ducard, G. and Geering, H.P. (2006). A reconfigurable flight control system based on the EMMAE method, Proceedings of the IEEE American Control Conference, Minneapolis, MN, USA, pp. 5499-5504. 
  18. Ducard, G. and Geering, H.P. (2008). Efficient nonlinear actuator fault detection and isolation system for unmanned aerial vehicles, AIAA Journal of Guidance, Control, and Dynamics 31(1): 225-237. 
  19. Ducard, G. and Geering, H.P. (2010). SMAC-FDI: New active fault detection and isolation scheme with high computational efficiency, Proceedings of the IEEE 2010 Conference on Control and Fault Tolerant Systems, Nice, France, pp. 30-37. 
  20. Eide, P. and Maybeck, P.S. (1996). An MMAE failure detection system for the F-16, IEEE Transactions on Aerospace and Electronic Systems 32(3): 1125-1136. 
  21. Elgersma, M., Enns, D., Shald, S. and Voulgaris, P. (1998). Parameter identification for systems with redundant actuators, Proceedings of the AIAA Guidance, Navigation and Control Conference and Exhibit, Boston, MA, USA, pp. 109-117. 
  22. Elgersma, M. and Glavaski, S. (2001). Reconfigurable control for active management of aircraft system failures, Proceedings of IEEE American Control Conference, Arlington, VA, USA, pp. 2627-2639. 
  23. Fekri, S., Athans, M. and Pascoal, A. (2006). Issues, progress and new results in robust adaptive control, International Journal of Adaptive Control and Signal Processing 20(10): 519-579. Zbl1132.93023
  24. Isermann, R. (2006). Fault-Diagnosis Systems, An Introduction from Fault Detection to Fault Tolerance, Springer-Verlag, Berlin/Heidelberg. 
  25. Magill, D.T. (1965). Optimal adaptive estimation of sampled stochastic processes, IEEE Transactions on Automatic Control 10(4): 434-439. 
  26. Maybeck, P.S. (1999). Multiple model adaptive algorithms for detecting and compensating sensor and actuator/surface failures in aircraft flight control systems, International Journal of Robust and Nonlinear Control 9(14): 1051-1070. 
  27. Maybeck, P.S. and Stevens, R.D. (1991). Reconfigurable flight control via multiple model adaptive control methods, IEEE Transactions on Aerospace and Electronic Systems 27(3): 470-479. 
  28. Möckli, M. (2006). Guidance and Control for Aerobatic Maneuvers of an Unmanned Airplane, Ph.D. thesis, Diss. No. 16586, ETH Zurich, Zurich. 
  29. Patton, R.J. and Chen, J. (1997). Observer-based fault detection and isolation: Robustness and applications, Control Engineering Practice 5(5): 671-682. 
  30. Patton, R.J., Uppal, F.J., Simani, S. and Polle, B. (2008). Reliable fault diagnosis scheme for a spacecraft control system, Journal of Risk and Reliability 222(2): 139-152, DOI: 10.1243/1748006XJRR98. 
  31. Rodrigues, M., Theilliol, D., Aberkane, S. and Sauter, D. (2007). Fault tolerant control design for polytopic LPV systems, International Journal of Applied Mathematics and Computer Science 17(1): 27-37, DOI: 10.2478/v10006-007-0004-5. Zbl1122.93073
  32. Simani, S., Fantuzzi, C. and Patton, R. (2003). ModelBased Fault Diagnosis in Dynamic Systems Using Identification Techniques, Advances in Industrial Control, Springer-Verlag, London. 
  33. Tanaka, N., Suzuki, S., Masui, K. and Tomita, H. (2006). Restructurable guidance and control for aircraft with failures considering gusts effects, AIAA Journal of Guidance, Control, and Dynamics 29(3): 635-642. 
  34. Theilliol, D., Join, C. and Zhang, Y. (2008). Actuator fault tolerant control design based on a reconfigurable reference input, International Journal of Applied Mathematics and Computer Science 18(4): 553-560, DOI:10.2478/v10006008-0048-1. Zbl1155.93402
  35. Zhang, Y. and Jiang, J. (2000). Integrated design of reconfigurable fault-tolerant control systems, AIAA Journal of Guidance, Control, and Dynamics 24(1): 133-136. 

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