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Direct Identification of Continuous-time Models from Sampled Data: Issues, Basic Solutions and Relevance

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Identification of Continuous-time Models from Sampled Data

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

Abstract

Mathematical models of dynamic systems are required in most areas of scientific enquiry and take various forms, such as differential equations, difference equations, state-space equations and transfer functions. The most widely used approach to mathematical modelling involves the construction of mathematical equations based on physical laws that are known to govern the behaviour of the system. Amongst the drawbacks to this approach are that the resulting models are often complex and not easily estimated directly from the available data because of identifiability problems caused by over-parameterisation. This complexity also makes them difficult to use in applications such as control system design.

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Author information

Authors and Affiliations

  1. Centre de Recherche en Automatique de Nancy, Nancy-Université, CNRS, France

    Hugues Garnier

  2. RMIT University, Melbourne, Australia

    Liuping Wang

  3. Lancaster University, UK

    Peter C. Young

  4. Australian National University, Australia

    Peter C. Young

Authors
  1. Hugues Garnier
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  2. Liuping Wang
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  3. Peter C. Young
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Editor information

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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© 2008 Springer-Verlag London Limited

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Garnier, H., Wang, L., Young, P.C. (2008). Direct Identification of Continuous-time Models from Sampled Data: Issues, Basic Solutions and Relevance. 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_1

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  • DOI: https://doi.org/10.1007/978-1-84800-161-9_1

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-160-2

  • Online ISBN: 978-1-84800-161-9

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