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Identification of continuous-time state-space models from non-uniform fast-sampled data

Identification of continuous-time state-space models from non-uniform fast-sampled data

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In this study, we apply the expectation-maximisation (EM) algorithm to identify continuous-time state-space models from non-uniformly fast-sampled data. The sampling intervals are assumed to be small and uniformly bounded. The authors use a parameterisation of the sampled-data model in incremental form in order to modify the standard formulation of the EM algorithm for discrete-time models. The parameters of the incremental model converge to the parameter of the continuous-time system description as the sampling period goes to zero. The benefits of the proposed algorithm are successfully demonstrated via simulation studies.

References

    1. 1)
      • G.C. Goodwin , S. Graebe , M.E. Salgado . (2001) Control system design.
    2. 2)
    3. 3)
      • J. Durbin , S.J. Koopman . (2005) Time series analysis by state space methods.
    4. 4)
      • G.C. Goodwin , R.L. Payne . (1977) Dynamic system identification: experiment design and data analysis.
    5. 5)
      • M. Deistler , G.C. Goodwin . (2000) System identification-general aspects and structure, Model identification and adaptive control: from windsurfing to telecommunications.
    6. 6)
      • McKelvey, T., Helmersson, A.: `State space parameterization of multivariable linear systems using tridiagonal matrix form', 35thIEEE Conf. on Decision and Control, 1996.
    7. 7)
      • A.P. Dempster , N.M. Laird , D.B. Rubin . Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. B , 1 , 1 - 38
    8. 8)
    9. 9)
    10. 10)
      • Agüero, J.C., Yuz, J.I., Goodwin, G.C.: `Frequency domain identification of MIMO state space models using the EM algorithm', European Control Conf. – ECC’07, 2007, Kos, Greece.
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
      • R.H. Middleton , G.C. Goodwin . (1990) Digital control and estimation: a unified approach.
    16. 16)
      • A. Feuer , G.C. Goodwin . (1996) Sampling in digital signal processing and control.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • M. Mansour . (1993) Stability and robust stability of discrete-time systems in the δ-transform, Fundamentals of discrete-time systems: a tribute to Prof. Eliahu I. Jury.
    22. 22)
    23. 23)
    24. 24)
    25. 25)
      • R. Shumway , D. Stoffer . (2006) Time series analysis and its applications.
    26. 26)
      • A.H. Jazwinski . (1970) Stochastic processes and filtering theory.
    27. 27)
      • P.E. Kloeden , E. Platen . (1992) Numerical solution of stochastic differential equations.
    28. 28)
      • B. Øksendal . (2003) Stochastic differential equations: an introduction with applications.
    29. 29)
      • K. Aström . (1970) Introduction to stochastic control theory.
    30. 30)
    31. 31)
      • R. Pintelon , J. Schoukens . (2001) System identification: a frequency domain approach.
    32. 32)
    33. 33)
    34. 34)
    35. 35)
    36. 36)
      • Agüero, J.C., Yuz, J.I., Goodwin, G.C., Tang, W.: `Identification of state-space systems using a dual time-frequency domain approach', 49thIEEE Conf. on Decision and Control, 2010.
    37. 37)
    38. 38)
    39. 39)
      • A. Bryson , Y. Ho . (1975) Applied optimal control: optimization, estimation, and control.
    40. 40)
    41. 41)
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