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Detection and isolation of incipient sensor faults for a class of uncertain non-linear systems

Detection and isolation of incipient sensor faults for a class of uncertain non-linear systems

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The present study proposes a new scheme for detection and isolation of incipient sensor faults for a class of uncertain non-linear systems by combining sliding mode observers (SMOs) with a Luenberger observer. Initially, a state and output transformation is introduced to transform the original system into two subsystems such that the first subsystem (subsystem-1) has system uncertainties but is free from sensor faults and the second subsystem (subsystem-2) has sensor faults but without any uncertainties. The sensor faults in subsystem-2 are then transformed to actuator faults using integral observer-based approach. The states of subsystem-1 are estimated using an SMO to eliminate the effects of uncertainties. However, since subsystem-2 does not have any uncertainties, the incipient faults present in this subsystem are detected by designing a Luenberger observer. These faults are then isolated by applying a bank of SMOs to subsystem-2. The sufficient condition of stability of the proposed scheme has been derived and expressed as linear matrix inequalities (LMIs). The design parameters of the observers are determined by using LMI techniques. The effectiveness of the proposed scheme in detecting and isolating sensor faults is illustrated considering an example of a single-link robotic arm with revolute elastic joint. The results of the simulation demonstrate that the proposed scheme can successfully detect and isolate sensor faults even in the presence of system uncertainties.

References

    1. 1)
    2. 2)
      • R. Raoufi , H.J. Marquez , A.S.I. Zinober . H∞ sliding mode observer for uncertain non-linear Lipschitz systems with fault estimation synthesis. Int. J. Robust Nonlinear Control , 1785 - 1801
    3. 3)
      • C. Edwards , S.K. Spurgeon . (1998) Sliding mode control: theory and applications.
    4. 4)
    5. 5)
      • Sreedhar, R., Fernandez, B., Masada, G.: `Robust fault detection in non-linear systems using sliding mode observers', Proc. IEEE Conf. Control Application, 1993.
    6. 6)
    7. 7)
    8. 8)
      • Raoufi, R., Marquez, H.J.: `Simultaneous sensor and actuator fault reconstruction and diagnosis using generalized sliding mode observers', Proc. American Control Conf., 2010, p. 7016–7021.
    9. 9)
    10. 10)
    11. 11)
    12. 12)
      • V.I. Utkin . (1992) Sliding modes in control optimization.
    13. 13)
    14. 14)
      • P. Ioannou , J. Sun . (1996) Robust adaptive control.
    15. 15)
      • V.I. Utkin . (1978) Sliding modes and their application in variable structure systems.
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
      • S. Simani , C. Fantuzzi , R.J. Patton . (2003) Model-based fault diagnosis in dynamic systems using identification techniques.
    21. 21)
      • R. Marino , P. Tomei . (1985) Nonlinear control design: geometric, adaptive and robust.
    22. 22)
    23. 23)
      • R. Yusof , R.Z.A. Rahman , M. Khalid . Fault detection and diagnosis for process control rig using artificial intelligent. ICIC Express Lett. , 1811 - 1816
    24. 24)
    25. 25)
      • Hermans, F., Zarrop, M.: `Sliding mode observers for robust sensor monitoring', Proc. 13th IFAC World Congress, 1996.
    26. 26)
    27. 27)
      • J. Chen , R.J. Patton . (1999) Robust model-based fault diagnosis for dynamic systems.
    28. 28)
    29. 29)
    30. 30)
      • Y. Wang , W. Wang , D. Wang . LMI approach to design fault detection filter for discrete-time switched systems with state delays. Int. J. Innov. Comput. and Inf. Control , 2 , 387 - 398
    31. 31)
    32. 32)
      • R. Isermann . (2006) Fault diagnosis of technical process-applications.
    33. 33)
      • S. Hui , S.H. Zak . Observer design for system with unknown inputs. Int. J. Appl. Math. Comput. Sci , 4 , 431 - 446
    34. 34)
      • Q. Ding , M.Y. Zhong . On designing H∞ fault detection filter for markovian jump linear systems with polytopic uncertainties. Int. J. Innov. Comput., Inf. Control , 995 - 1004
    35. 35)
    36. 36)
    37. 37)
    38. 38)
    39. 39)
      • R.J. Patton , P.M. Frank , R.N. Clark . (1989) Fault diagnosis in dynamic systems: theory and applications.
    40. 40)
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