Sliding Mode Observers for Fault Estimation in Multisensor Avionics Systems

  • Jérome CieslakEmail author
  • Alejandra Ferreira de Loza
  • David Henry
  • Jorge Dávila
  • Ali Zolghadri
Conference paper


The paper addresses the problem of sensor fault estimation in avionics multisensor systems. Under the assumption of system strong observability, sliding mode observers are designed to estimate the faults in finite time and in the presence of bounded disturbances. It is shown that the fault estimation error is bounded in the L  ∞ -norm sense, and an upper bound is theoretically derived. The method is applied to the problem of sensor fault estimation of a large transport aircraft. Simulation results as well as a pilot experiment are presented to demonstrate the potential of the proposed method.


Unknown Input Sensor Fault Fault Estimation Slide Mode Observer Aircraft Model 
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  1. 1.
    Berdjag, D., Cieslak, J., Zolghadri, A.: Fault diagnosis and monitoring of oscillatory failure case in aircraft inertial system. Control Engineering Practice 20, 1410–1425 (2012)CrossRefGoogle Scholar
  2. 2.
    Allerton, D., Jia, H.: Distributed data fusion algorithms for inertial network systems. IET Radar, Sonar and Navigation 2, 51–62 (2008)CrossRefGoogle Scholar
  3. 3.
    Henry, D., Cieslak, J., Zolghadri, A., Efimov, D.: A non-conservative H  − /H  ∞  solution for early and robust fault diagnosis in aircraft control surface servo-loops. Control Engineering Practice 31, 183–199 (2014)CrossRefGoogle Scholar
  4. 4.
    Oosterom, M., Babuska, R., Verbruggen, H.: Soft computing applications in aircraft sensor management and flight control law reconfiguration. IEEE Transactions on Systems Man, and Cybernetics, Part C (Applications and Reviews) 32, 125–139 (2008)CrossRefGoogle Scholar
  5. 5.
    Hegg, J.: Enhanced space integrated GPS/INS (SIGI). IEEE Aerospace and Electronic Systems Magazine 17, 26–33 (2002)CrossRefGoogle Scholar
  6. 6.
    Zolghadri, A., Henry, D., Cieslak, J., Efimov, D., Goupil, P.: Fault Diagnosis and Fault-Tolerant Control and Guidance for Aerospace Vehicles: From theory to application. Series: Advances in Industrial Control. Springer (2014) ISBN 978-1-4471-5312-2Google Scholar
  7. 7.
    Balaban, E., Saxena, A., Bansal, P., Goebel, K., Curran, S.: Modeling, detection, and disambiguation of sensor faults for aerospace applications. IEEE Sensors Journal 9, 1907–1917 (2009)CrossRefGoogle Scholar
  8. 8.
    Tan, C.P., Edwards, C.: Sliding mode observers for detection and reconstruction of sensor faults. Automatica 38, 1815 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Utkin, V.I.: Sliding modes in control and optimization. Springer, Berlin (1992)CrossRefzbMATHGoogle Scholar
  10. 10.
    Levant, A.: Higher-order sliding modes, differentiation and output-feedback control. International Journal of Control 76, 924–941 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Ferrara, A., Pisu, P.: Minimum sensor second-order sliding mode longitudinal control of passenger vehicles. IEEE Transactions on Intelligent Transportation 5, 20–32 (2004)CrossRefGoogle Scholar
  12. 12.
    Cieslak, J., Efimov, D., Zolghadri, A., Henry, D., Goupil, P.: Design of a non-homogeneous differentiator for actuator oscillatory failure case reconstruction in noisy environment. Proc. IMechE Part I: J. Systems and Control Engineering (2014), doi:10.1177/0959651814561091Google Scholar
  13. 13.
    Imine, H., Madani, T., Srairi, S.: High order sliding mode observer to estimate vertical forces: experimental results. In: 11th International IEEE Conf. on Intelligent Transportation Systems, pp. 523–527 (2008)Google Scholar
  14. 14.
    Edwards, C., Tan, C.P.: Sensor fault tolerant control using sliding mode observers. Control Engineering Practice 14, 897–908 (2006)CrossRefGoogle Scholar
  15. 15.
    Fridman, L., Levant, A., Davila, J.: Observation of linear systems with unknown inputs via high-order sliding-modes. International Journal of Systems Science 38, 773–791 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Bejarano, F., Fridman, L.: Output integral sliding mode control based on algebraic hierarchical observer. International Journal of Control 9, 1920–1929 (2010)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Fridman, L., Davila, J., Levant, A.: High-order sliding-mode observation for linear systems with unknown inputs. Nonlinear Analysis: Hybrid Systems 5(2), 189–205 (2011)zbMATHMathSciNetGoogle Scholar
  18. 18.
    Kolmogorov, A.N.: On inequalities between upper bounds of consecutive derivatives of an arbitrary function defined on an infinite interval. American Mathematical Society Translations, 233–242 (1962)Google Scholar
  19. 19.
    Bejarano, F., Figueroa, M., Pacheco, J., Rubio, J.D.J.: Robust fault diagnosis of disturbed linear systems via a sliding mode high order differentiator. International Journal of Control 85, 648–659 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Levant, A.: Non-homogeneous finite-time convergent differentiator. In: Proc. of the Conference on Decision and Control, pp. 8399–8404 (2009)Google Scholar
  21. 21.
    Edwards, C., Lombaerts, T., Smaili, H.: Fault Tolerant Flight Control: A Benchmark Challenge. Springer (2010)Google Scholar
  22. 22.
    Alwi, H., Edwards, C., Tan, C.: Sliding mode observers for detection and reconstruction of sensor faults. Automatica 45, 1679–1685 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Fridman, L., Levant, A., Davila, J.: Observation of linear systems with unknown inputs via high-order sliding-modes. Int. J. System Science 38(10), 773–791 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  24. 24.
    Bejarano, F., Fridman, L., Poznyak, A.: Exact state estimation for linear systems with unknown inputs based on hierarchical super-twisting algorithm. Intern. J. of Robust and Nonlinear Control 17, 1734–1753 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  25. 25.
    Molinari, B.P.: A strong controllability and observability in linear multivariable control. IEEE Transactions on Automatic Control 21, 761–764 (1976)CrossRefzbMATHMathSciNetGoogle Scholar
  26. 26.
    Van Der Linden, C.: DASMAT-Delft university aircraft simulation model and analysis tool. Report LR-781 Technical University Delft (1996)Google Scholar
  27. 27.
    Goupil, P., Marcos, A.: The european addsafe project: Industrial and academic efforts towards advanced fault diagnosis. Control Engineering Practice (2014)Google Scholar
  28. 28.
    Cieslak, J., Henry, D., Zolghadri, A.: Fault tolerant flight control: from theory to piloted flight simulator experiments. IET Control Theory and Applications 4, 1451–1464 (2010)CrossRefGoogle Scholar
  29. 29.
    Cieslak, J., Henry, D., Zolghadri, A., Goupil, P.: Development of an active fault-tolerant flight control strategy. Journal of Guidance, Control and Dynamics 31, 135–147 (2008)CrossRefGoogle Scholar
  30. 30.
    Basseville, M., Nikiforov, I.: Detection of abrupt changes. Theory and application. Prentice Hall Information and System Sciences Series (1993)Google Scholar
  31. 31.
    Stroosma, O., Van Paassen, M., Mulder, M.: Using the simona research simulator for humanmachine interaction research. In: AIAA Modeling and Simulation Technologies Conf. (2003)Google Scholar
  32. 32.
    Koekebakker, S.: Model based control of a flight simulator motion base, Delft, Netherlands (2001)Google Scholar
  33. 33.
    Berkouwer, W., Stroosma, O., Van Paassen, M., Mulder, M., Mulder, J.: Measuring the performance of the simona research simulators motion system. In: AIAA Modeling and Simulation Conf. (2005)Google Scholar
  34. 34.
    Stroosma, O., Smaili, H., Mulder, J.: Pilot-in-the-loop evaluation of fault-tolerant flight control systems. In: SAFEPROCESS 2009 (2009)Google Scholar
  35. 35.
    Ferreira de Loza, A., Cieslak, J., Henry, D., Dávila, J., Zolghadri, A.: Sensor fault diagnosis using a non-homogeneous high-order sliding mode observer with application to a transport aircraft. IET Control Theory and Applications (2015), doi:10.1049/iet-cta.2014.0226Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jérome Cieslak
    • 1
    Email author
  • Alejandra Ferreira de Loza
    • 2
  • David Henry
    • 1
  • Jorge Dávila
    • 3
  • Ali Zolghadri
    • 1
  1. 1.CNRS, IMS-labUniversité de BordeauxTalence cedexFrance
  2. 2.Instituto Politecnico NacionalTijuanaMexico
  3. 3.Section of Graduate Studies and Research, ESIME-UPTNational Polytechnic InstituteMéxico D.F.México

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