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Refined Instrumental Variable Identification of Continuous-time Hybrid Box-Jenkins Models

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Identification of Continuous-time Models from Sampled Data

Part of the book series: Advances in Industrial Control ((AIC))

Abstract

This chapter describes and evaluates a statistically optimal method for the identification and estimation3 of continuous-time (CT) hybrid Box-Jenkins (BJ) transfer function models from discrete-time, sampled data. Here, the model of the basic dynamic system is estimated in continuous-time, differential equation form, while the associated additive noise model is estimated as a discrete-time, autoregressive moving average (ARMA) process. This refined instrumental variable method for continuous-time systems (RIVC) was first developed in 1980 by Young and Jakeman [52] and its simplest embodiment, the simplified RIVC (SRIVC) method, has been used successfully for many years, demonstrating the advantages that this stochastic formulation of the continuous-time estimation problem provides in practical applications (see, e.g., some recent such examples in [16, 34, 40, 45, 48]).

The statistical meaning of these terms will be used here, where ‘identification’ is taken to mean the specification of an identifiable model structure and ‘estimation’ relates to the estimation of the parameters that characterise this identified model structure.

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Author information

Authors and Affiliations

  1. Lancaster University, UK

    Peter C. Young

  2. Australian National University, Australia

    Peter C. Young

  3. Nancy-Université, CNRS, France

    Hugues Garnier & Marion Gilson

Authors
  1. Peter C. Young
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  2. Hugues Garnier
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  3. Marion Gilson
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Editors and Affiliations

  1. Faculté des Sciences et Techniques, Centre de Recherche en Automatique de Nancy (CRAN), Nancy-Université CNRS, 54506, Vandoeuvre-les-Nancy, France

    Hugues Garnier PhD

  2. School of Electrical and Computing Engineering, RMIT University, Swanston Street, Melbourne, 3000, Victoria, Australia

    Liuping Wang PhD

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Young, P.C., Garnier, H., Gilson, M. (2008). Refined Instrumental Variable Identification of Continuous-time Hybrid Box-Jenkins Models. In: Garnier, H., Wang, L. (eds) Identification of Continuous-time Models from Sampled Data. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-84800-161-9_4

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  • DOI: https://doi.org/10.1007/978-1-84800-161-9_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-160-2

  • Online ISBN: 978-1-84800-161-9

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