Identification of Continuous-time Models from Sampled Data

  • Hugues Garnier
  • Liuping Wang

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

Table of contents

  1. Front Matter
    Pages i-xxvi
  2. Erik K. Larsson, Magnus Mossberg, Torsten Söderström
    Pages 31-66
  3. Juan I. Yuz, Graham C. Goodwin
    Pages 67-89
  4. Peter C. Young, Hugues Garnier, Marion Gilson
    Pages 91-131
  5. Marion Gilson, Hugues Garnier, Peter C. Young, Paul Van den Hof
    Pages 133-160
  6. Liuping Wang, Peter C. Young
    Pages 161-187
  7. Hugues Garnier, Marion Gilson, Thierry Bastogne, Michel Mensler
    Pages 249-290
  8. Rolf Johansson
    Pages 291-311
  9. Salim Ahmed, Biao Huang, Sirish L. Shah
    Pages 313-337
  10. Back Matter
    Pages 409-411

About this book


System identification is an established field in the area of system analysis and control. It aims to determine particular models for dynamical systems based on observed inputs and outputs. Although dynamical systems in the physical world are naturally described in the continuous-time domain, most system identification schemes have been based on discrete-time models without concern for the merits of natural continuous-time model descriptions. The continuous-time nature of physical laws, the persistent popularity of predominantly continuous-time proportional-integral-derivative control and the more direct nature of continuous-time fault diagnosis methods make continuous-time modeling of ongoing importance.

Identification of Continuous-time Models from Sampled Data brings together contributions from well-known experts who present an up-to-date view of this active area of research and describe recent methods and software tools developed in this field. They offer a fresh look at and new results in areas such as:

• time and frequency domain optimal statistical approaches to identification;

• parametric identification for linear, nonlinear and stochastic systems;

• identification using instrumental variable, subspace and data compression methods;

• closed-loop and robust identification; and

• continuous-time modeling from non-uniformly sampled data and for systems with delay.

The Continuous-Time System Identification (CONTSID) toolbox described in the book gives an overview of developments and practical examples in which MATLAB® can be brought to bear in the cause of direct time-domain identification of continuous-time systems.This survey of methods and results in continuous-time system identification will be a valuable reference for a broad audience drawn from researchers and graduate students in signal processing as well as in systems and control. It also covers comprehensive material suitable for specialised graduate courses in these areas.


Analysis MATLAB Signal data compression model modeling system analysis

Editors and affiliations

  • Hugues Garnier
    • 1
  • Liuping Wang
    • 2
  1. 1.Faculté des Sciences et TechniquesCentre de Recherche en Automatique de Nancy (CRAN), Nancy-Université CNRSVandoeuvre-les-NancyFrance
  2. 2.School of Electrical and Computing EngineeringRMIT UniversityMelbourneAustralia

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag London Limited 2008
  • Publisher Name Springer, London
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-1-84800-160-2
  • Online ISBN 978-1-84800-161-9
  • Series Print ISSN 1430-9491
  • Series Online ISSN 2193-1577
  • Buy this book on publisher's site