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Identification of continuous-time systems: direct or indirect?

Autorzy
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper summarizes and discusses the results of an extensive simulation based exercise in quest of dependable approaches to identification of continuous-time models from sampled input/output data of continuous-time dynamical systems. Two well-established approaches are considered together with several related techniques of parameter estimation. One is an indirect approach in which well-established discrete-time techniques are applied to first estimate a discrete-time model for the original continuous-time system and the model is then transformed into a continuous-time version. The other is a direct approach in which a continuous-time model is estimated straightaway using well-known continuous-time methods. On the surface, the choice between the two approaches may seem trivial but this paper underlines the need to establish dependability of any approach in terms of certain criteria. The results of extensive simulations clearly show that the direct approach is more dependable than the indirect route.
Czasopismo
Rocznik
Strony
25--50
Opis fizyczny
Bibliogr. 23 poz., wykr.
Twórcy
autor
  • Advisor, UNESCO-EOLSS Joint Committee, P.O. Box 2623, Abu Dhabi, United Arab Emirates
autor
  • Centre de Recherche en Automatique de Nancy CNRS UMR 7039, Universite Henri Poincare, Nancy 1, BP 239, 54506 Vandoeuvre-les-Nancy Cedex, France
Bibliografia
  • [1] Astrom K. J., Hagander P., Sternby J., Zeros of sampled systems, Automatica, Vol. 20, No. 1, 1984,31-38.
  • [2] Bastogne T., Garnier H., Sibille P., A PMF-based subspace method for continuous-time model identification. Application to a multivariable winding process. International Journal of Control, Vol. 74, No. 2, 2001, 118-132.
  • [3] Garnier H., Mensler M., The CONTSID toolbox: a Matlab toolbox for CONtinuous-Time System Identification, 12th IFAC Symposium on System Identification, Santa Barbara, 2000.
  • [4] Garnier H., Gilson M., Huselsein E., Developments for the Matlab CONTSID toolbox, 13th IFAC Symposium on System Identification, Rotterdam, 2003.
  • [5] Garnier H., Gilson M., Zheng W. X., A bias-eliminated least-squares method for continuous-time model identification of closed-loop systems. International Journal of Control, Vol. 73, No. 1, 2000, 38-48.
  • [6] Garnier H., Mensler M., Richard A., Continuous-time model identification from sampled data. Implementation issues and performance evaluation. International Journal of Control, Vol. 76, No. 13, 2003, 1337-1357.
  • [7] Huselstein E., Garnier H., Richard A., Young P. C., La boite a outils CONTSID d'identification de modeles a temps continu - Extensions recentes. Conference Internationale Francophone d'Automatique, Nantes, 2002.
  • [8] Lung L., System identification. Theory for the user, Prentice-Hall, 2nd edition, 1999.
  • [9] Lung L., Initialisation aspects for subspace and output-error identification methods, European Control Conference, Cambridge, 2003.
  • [10] Middleton R. H., Goodwin G. C., Digital control and estimation-A unified approach, Prentice-Hall, 1990.
  • [11] Pearson A. E., Shen Y., Weighted least squares/mft algorithms for linear differential system identification, 32nd IEEE Conference on Decision and Control, San Antonio, 1993, 2032-2037
  • [12] Pintelon R., Schoukens J., Rolain Y., Box-Jenkins continuous-time modeling, Automatica, Vol. 36, 2000, 983-991.
  • [13] Rao G. P., Garnier H., Numerical illustrations of the relevance of direct continuous-time model identification, 15th Triennial IFAC World Congress on Automatic Control, Barcelona, 2002.
  • [14] Sagara S., Zhao Z. Y., Numerical integration approach to on-line identification of continuous-time systems, Automatica, Vol. 26, No. 1, 1990, 63-74.
  • [15] Schoukens J., Pintelon R., van Hamme H., Identification of linear dynamic systems using piece-wise constant excitations: use, misuse and alternatives, Automatica, Vol. 30, No. 7, 1994, 1953-1169.
  • [16] Sinha N. K., Rao G. P., Identification of continuous-time systems. Methodology and computer implementation, Kluwer Academic Publishers, 1991.
  • [17] Soderstrom T., Mossberg M., Performance evaluation of methods for identifying continuous-time autoregressive processes, Automatica, Vol. 36, 2000, 53-59.
  • [18] Soderstrom T., Stoica P., System Identification, Prentice-Hall, 1989.
  • [19] Unbehauen H., Rao G. P., Identification of continuous systems, North-Holland, Amsterdam, 1987.
  • [20] Unbehauen H., Rao G. P., Continuous-time approaches to system identification - a survey, Automatica Vol. 26, No. 1, 1990, 23-35.
  • [21] Young P. C., Parameter estimation for continuous-time models - a survey, Automatica, Vol. 17, No. 1, 1981,23-39.
  • [22] Young P. C., Comments on "On the estimation of continuous-time transfer functions". International Journal of Control, Vol. 75, No. 9, 2002, 693-697.
  • [23] Young P. C., Optimal IV identification and estimation of continuous-time TF models, 15th Triennial IFAC World Congress on Automatic Control, Barcelona, 2002.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0008-0035
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