Computationally efficient IV algorithm for structure and parameter identification of linear systems

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Computationally efficient IV algorithm for structure and parameter identification of linear systems

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A computationally efficient procedure for identifying the structure and parameters of a linear discrete-time SISO system is presented. The procedure is based upon a new computational technique of parameter estimation, using the instrumental variables, that eliminates a number of redundant computations and does not require explicit evaluation of the instrumental variables. The structure determination is aimed at providing a combination of low residual error, low output error and low parameter estimation error. Application of the proposed procedure on simulated, as well as real, data is reported.

Inspec keywords: discrete time systems; parameter estimation; linear systems

Other keywords: IV algorithm; SISO system; discrete time system; structure identification; parameter identification; parameter estimation; linear systems

Subjects: Discrete control systems; Simulation, modelling and identification

References

    1. 1)
      • P.C. Young , A. Jakeman , R. McMurtrie . An instrumental variable method for model order identification. Automatica , 281 - 294
    2. 2)
      • P.C. Young . An instrumental variable method for real-time identification of a noisy process. Automatica , 271 - 287
    3. 3)
      • G.E.P. Box , G.M. Jenkins . (1970) , Time series analysis forecasting and control.
    4. 4)
      • H. Akaike , P.R. Krishnaiah . (1977) On entropy maximization principle, Application of statistics.
    5. 5)
      • K.J. Astrom , P. Eykhoff . System identification-a survey. Automatica , 123 - 162
    6. 6)
      • Ahmed, M.S.: `State variable model identification of multivariable systems by correlation analysis', Proceedings of 12th annual conference on modelling and simulation, 1981, Pittsburgh, p. 739–745.
    7. 7)
      • E. Tse , H.L. Weinert . Structure determination and parameter identification for multivariable stochastic linear systems. IEEE Trans. , 603 - 613
    8. 8)
      • van den Boom, A.J.W., van den Eden, A.W.M.: `The determination of the order of the process and noise dynamics', Proceedings of 3rd IFAC symposium on identification and system parameter estimation, 1973, The Hague, p. 929–938.
    9. 9)
      • C. Chatfield . Some recent developments in time series analysis. Appl. Stat. , 492 - 510
    10. 10)
      • A.J. Jakeman , L.P. Steele , P.C. Young . Instrumental variable algorithms for multiple input systems described by multiple transfer functions. IEEE Trans. , 593 - 602
    11. 11)
      • P.C. Young , S.H. Shellswell . Review of time series analysis, forecasting and control. IEEE Trans. , 281 - 282
    12. 12)
      • Talmon, J.C., van den Boom, A.J.W.: `On the estimation of the transfer function parameters of process and noise dynamics using a single stage estimator', Proceedings of 3rd IFAC symposium on system identification and system parameter estimation, 1973, The Hague, p. 711–720.
    13. 13)
      • R. Guidorzi . Canonical structures in the identification of multivariable systems. Automation , 361 - 374
    14. 14)
      • R. Isermann , U. Bauer , W. Bamberger , P. Kneppo , H. Siebert . Comparison of six on-line identification and parameter estimation methods. Automatica , 81 - 103
    15. 15)
      • J. Rissanen , R.K. Mehra , D.G. Laniatis . (1976) Minimax entropy estimation of models for vector process, System identification: advances and case studies.
    16. 16)
      • T. SÖDERSTRÖM , P. STOICA . (1983) , Instrumental variable methods for system identification.
    17. 17)
      • Clarke, D.W.: `Generalized least squares estimation of a dynamic model', Paper 3.17, Preprint of IFAC symposium on identification and system parameter estimation, 1967, Prague.
    18. 18)
      • M.S. Ahmed . Estimation of difference equation parameters of SISO systems by correlation analysis. Int. J. Control , 677 - 687
    19. 19)
      • M.S. Ahmed . Fast GLS algorithm for parameter estimation. Automatica , 231 - 236
    20. 20)
      • Y.S. Yuan , W.M. Wonham . Probing signals for model reference identification. IEEE Trans. , 530 - 538
    21. 21)
      • K.Y. Wong , E. Polak . Identification of linear discrete time systems using an instrumental variable method. IEEE Trans. , 707 - 719
    22. 22)
      • C.M. Woodside . Estimation of the order of linear systems. Automation , 727 - 733
    23. 23)
      • P.C. Young , A.J. Jakeman . Refined instrumental variable methods of recursive time-series analysis: Part 1: Single input single output systems. Int. J. Control , 1 - 30
    24. 24)
      • T. Soderstrom , P. Stoica . Comparison of some instrumental variable methods-consistency and accuracy aspects. Automatica , 101 - 115
    25. 25)
      • R. Iserman , B. Bauer . Two-step process identification with correlation analysis and least squares parameter estimation. Trans. ASME Ser. G, J. Dyn. Syst. Meas. & Control , 425 - 432
    26. 26)
      • R.C.K. Lee . (1964) , Optimal estimation, identification and control.
    27. 27)
      • P.E. Wellstead . An instrumental product moment test for model order estimation. Automation , 89 - 91
    28. 28)
      • Ahmed, M.S.: `Structure determination and parameter estimation of multivariable systems by instrumental variable method', Proceedings of IFAC 6th symposium on identification and process parameter estimation, June 1982, Washington, DC.
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