Brief Identification of dynamic errors-in-variables models: Approaches based on two-dimensional ARMA modeling of the data

  • Authors:

  • Torsten SöDerströM;Kaushik Mahata;Umberto Soverini

  • Affiliations:

  • Systems and Control, Department of Information Technology, Uppsala University, P.O. Box 337, SE-751 05 Uppsala, Sweden;Systems and Control, Department of Information Technology, Uppsala University, P.O. Box 337, SE-751 05 Uppsala, Sweden;DEIS - Dipartimento di Elettronica, Informatica e Sistemistica, Universití di Bologna, Viale Risorgimento 2, IT-40136 Bologna, Italy

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2003

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Abstract

In this paper we propose a parametric and a non-parametric identification algorithm for dynamic errors-in-variables model. We show that the two-dimensional process composed of the input-output data admits a finite order ARMA representation. The non-parametric method uses the ARMA structure to compute a consistent estimate of the joint spectrum of the input and the output. A Frisch scheme is then employed to extract an estimate of the joint spectrum of the noise free input-output data, which in turn is used to estimate the transfer function of the system. The parametric method exploits the ARMA structure to give estimates of the system parameters. The performances of the algorithms are illustrated using the results obtained from a numerical simulation study.