System Identification

An Introduction

  • Karel J. Keesman

Table of contents

  1. Front Matter
    Pages I-XXVI
  2. Karel J. Keesman
    Pages 1-13
  3. Data-based Identification

    1. Front Matter
      Pages 15-15
    2. Karel J. Keesman
      Pages 17-27
    3. Karel J. Keesman
      Pages 29-41
    4. Karel J. Keesman
      Pages 43-58
  4. Time-invariant Systems Identification

    1. Front Matter
      Pages 59-60
    2. Karel J. Keesman
      Pages 61-112
    3. Karel J. Keesman
      Pages 113-166
  5. Time-varying Systems Identification

    1. Front Matter
      Pages 167-167
    2. Karel J. Keesman
      Pages 169-193
    3. Karel J. Keesman
      Pages 195-221
  6. Model Validation

    1. Front Matter
      Pages 223-223
    2. Karel J. Keesman
      Pages 225-247
  7. Back Matter
    Pages 249-323

About this book

Introduction

System Identification: an Introduction shows the (student) reader how to approach the system identification problem in a systematic fashion. Essentially, system identification is an art of modelling, where appropriate choices have to be made concerning the level of approximation, given prior system’s knowledge, noisy data and the final modelling objective. The system identification process is basically divided into three steps: experimental design and data collection; model structure selection and parameter estimation; and model validation, each of which is the subject of one or more parts of the text.

The book contains four parts covering:

·        data-based identification – non-parametric methods for use when prior system knowledge is very limited;

·        time-invariant identification for systems with constant parameters;

·        time-varying systems identification, primarily with recursive estimation techniques; and

·        model validation methods.

The book uses essentially semi-physical or grey-box modelling methods although data-based, transfer-function system descriptions are also introduced. The approach is problem-based rather than rigorously mathematical. The use of finite input–output data is demonstrated for frequency- and time-domain identification in static, dynamic, linear, nonlinear, time-invariant and time-varying systems. Simple examples are used to show readers how to perform and emulate the identification steps involved in various model applications, as control, prediction and experimental design, with more complex illustrations derived from real physical, chemical and biological applications being used to demonstrate the practical applicability of the methods described. End-of-chapter exercises (for which a downloadable instructors’ Solutions Manual is available from www.springer.com/978-0-85729-521-7) will both help students to assimilate what they have learnt and make the book suitable for self-tuition by practitioners looking to brush up on modern techniques.

Graduate and final-year undergraduate students will find this text to be a practical and realistic course in system identification that can be used for assessing the processes of a variety of engineering disciplines. System Identification: an Introduction will help academic instructors teaching control-related courses to give their students a good understanding of identification methods that can be used in the real world without the encumbrance of undue mathematical detail.

 

Keywords

Control Applications Control Engineering Dynamic Systems Parameter Estimation Parameter Estimation Textbook Parameter Estimation Textbook System Identification System Identification Textbook System Identification Textbook Time Series

Authors and affiliations

  • Karel J. Keesman
    • 1
  1. 1.Systems and Control GroupWageningen UniversityWageningenNetherlands

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-85729-522-4
  • Copyright Information Springer-Verlag London Limited 2011
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-0-85729-521-7
  • Online ISBN 978-0-85729-522-4
  • Series Print ISSN 1439-2232
  • About this book