Multi-agent methodology for distributed and cooperative supervisory estimation subject to unreliable information

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Multi-agent methodology for distributed and cooperative supervisory estimation subject to unreliable information

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© The Institution of Engineering and Technology
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In this work, a novel multi-agent framework for cooperative supervisory estimation of linear time-invariant systems is proposed. This framework is developed based on the notion of subobservers and a discrete-event system (DES) supervisory control and is applicable to large-scale systems. We introduce a group of subobservers where each subobserver is estimating certain states that are conditioned on a given input, output and state information. The cooperation among the subobservers is managed by a DES supervisor. The supervisor makes decisions regarding the selection and configuration of a set of subobservers to successfully estimate all the system states, while the feasibility of the overall integrated cooperative subobservers is verified. When certain anomalies (faults) are present in the system, or the sensors and subobservers become unreliable, the supervisor reconfigures the set of selected subobservers so that the impacts of anomalies on the estimation performance are minimised to the extent that is possible. The application and capabilities of our proposed methodology in a practical industrial process is demonstrated through numerical simulations.

Inspec keywords: multi-agent systems; linear systems; observers; discrete event systems; large-scale systems; time-varying systems

Other keywords: subobserver; cooperative supervisory estimation; decision making; multiagent methodology; linear time-invariant systems; discrete-event system supervisory control; large-scale system; state estimation; industrial process

Subjects: Multivariable control systems; Simulation, modelling and identification; Time-varying control systems; Discrete control systems; Expert systems and other AI software and techniques

References

    1. 1)
      • Tousi, M.M., Khorasani, K.: `An optimal hybrid fault recovery for a team of unmanned vehicles', Proc. IEEE Systems Conf., March 2009, p. 241–246.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • Ferguson, P., How, J.: `Decentralized estimation algorithms for formation flying spacecraft', AIAA Guidance, Navigation and Control Conf., 2003, no. 2003–5442.
    6. 6)
      • Alighanbari, M., How, J.P.: `An unbiased kalman consensus algorithm', Proc. Am. Control Conf., 2006, p. 3519–3524.
    7. 7)
    8. 8)
    9. 9)
      • Y. Nakayama , R. Boucher . (1998) Introduction to fluid mechanics.
    10. 10)
    11. 11)
    12. 12)
      • Ren, W., Beard, R.W., Kingston, D.B.: `Multi-agent Kalman consensus with relative uncertainty', Proc. Am. Control Conf., 2005, 3, p. 1865–1870.
    13. 13)
    14. 14)
    15. 15)
      • Olfati-Saber, R.: `Distributed Kalman filtering for sensor networks', IEEE Conf. on Decision and Control, 2007, p. 5492–5498.
    16. 16)
    17. 17)
      • W.M. Wonham . (2009) Supervisory control of discrete-event systems.
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
      • C.-T. Chen . (1988) Linear system theory and design.
    24. 24)
    25. 25)
      • Y. Bar-Shalom , X.R. Li , T. Kirubarajan . (2001) Estimation with applications to tracking and navigation.
    26. 26)
    27. 27)
      • Boskovic, J.D., Mehra, R.K.: `Multi-mode switching in flight control', IEEE Digital Avionics Systems Conf., 2000, 2, p. 6F2/1–6F2/8.
    28. 28)
      • Commault, C., Dion, J.-M., Trinh, D.H.: `Observability recovering by additional sensor implementation in linear structured systems', IEEE Conf. Decision and Control and European Control Conf., 2005, p. 7193–7197.
    29. 29)
    30. 30)
    31. 31)
      • J. Chen , R.J. Patton . (1999) Robust model-based fault diagnosis for dynamic systems.
    32. 32)
    33. 33)
      • Ren, W., Beard, R., Atkins, E.: `A survey of consensus problems in multi-agent coordination', Proc. 2005 American Control Conf., May 2005, 3, p. 1859–1864.
    34. 34)
      • Azizi, S.M., Khorasani, K.: `A distributed Kalman filter for actuator fault estimation of deep space formation flying satellites', Proc. IEEE Systems Conf., March 2009, Vancouver, Canada.
    35. 35)
    36. 36)
    37. 37)
      • ‘http://hpsweb.honeywell.com/cultures/enus/products/wireless/xyr6000wireless/xyr6000differential/default.htm.’ [Online]. Available at: http://hpsweb.honeywell.com/Cultures/en-US/Products/Wireless/xyr6000wireless/xyr6000differential/default.htm.
    38. 38)
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