access icon free Distributed fault detection for a class of second-order multi-agent systems: an optimal robust observer approach

In this study, the distributed fault detection and isolation problem for a class of second-order discrete-time multi-agent systems (MASs) is studied by using an optimal robust observer approach. The work is based on an MAS with a mean square bounded consensus protocol. The authors prove that for this kind of system, a bank of optimal robust observers can be constructed for a certain agent to detect and isolate the possible faults of its neighbour agents. The main result of this study is to bring forward an existence condition for these observers, which is closely related to the topology structure of the MAS. For the detection of the faults injected in position states, they show that only the position information of the MAS is needed, but for the case of detecting faults in velocity states, both position and velocity measurements are needed. Simulation results are presented to illustrate the effectiveness of the results proposed in this study.

Inspec keywords: optimal control; multi-agent systems; fault diagnosis; discrete time systems

Other keywords: mean square bounded consensus protocol; MAS; topology structure; discrete-time multiagent system; velocity state; second-order multiagent system; velocity measurement; position measurement; distributed fault detection; optimal robust observer; fault isolation problem

Subjects: Optimal control; Discrete control systems; Artificial intelligence (theory)

References

    1. 1)
    2. 2)
      • 23. Chen, J., Patton, R.J.: ‘Robust model-based fault diagnosis techniques for dynamic systems’, (Springer Verlag, 1999, 1st edn.), pp. 7585.
    3. 3)
    4. 4)
      • 25. Saroj, B., Frank, F., Qing, D., Li, B.: ‘Resilient consensus control for linear systems in a noisy environment’, Proc. American Control Conf., Montreal, Canada, June 2012, pp. 58625867.
    5. 5)
    6. 6)
    7. 7)
      • 15. Zhang, X.D.: ‘Decentralized fault detection for a class of large-scale nonlinear uncertain systems’, Proc. American Control Conf., Baltimore, MD, USA, June 2010, pp. 56505655.
    8. 8)
      • 18. Azizi, S.M., Khorasani, K.: ‘A distributed kalman filter for actuator fault estimation of deep space formation flying satellites’, Proc. IEEE Systems Conf., Vancouver, Canada, March 2009, pp. 354359.
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
      • 30. Chen, J., Patton, R.J.: ‘Robust fault diagnosis of stochastic systems with unknown disturbances’, Proc. Int. Conf. Control, Coventry, UK, January 1994, pp. 13401345.
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
      • 20. Davoodi, M.R., Khorasani, K., Talebi, H.A., Momeni, H.R.: ‘A novel distributed robust fault detection and isolation filter design for a network of nonhomogeneous multi-agent systems’, Proc. IEEE Conf. on Decision and Control, Maui, Hawaii, USA, December 2012, pp. 592599.
    24. 24)
      • 14. Ding, S.X.: ‘Model-based fault diagnosis techniques and design schemes’, (Springer Verlag, 2008, 1st edn.), pp. 1321.
    25. 25)
    26. 26)
    27. 27)
    28. 28)
      • 24. Barooah, P., Hespanha, J.P.: ‘Graph effective resistance and distributed control: spectral properties and applications’, Proc. IEEE Conf. on Decision and Control, San Diego, USA, December 2006, pp. 34793485.
    29. 29)
      • 16. Jennings, N.R.: ‘Decentralized control of complex systems’, Proc. Int. Symp. on Computer and Information Sciences, Ankara, Turkey, September 2007, pp. 440442.
    30. 30)
    31. 31)
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