Table of contents

  1. Front Matter
    Pages I-XVII
  2. Juan I. Yuz, Graham C. Goodwin
    Pages 1-4
  3. Deterministic Systems

    1. Front Matter
      Pages 5-5
    2. Juan I. Yuz, Graham C. Goodwin
      Pages 7-19
    3. Juan I. Yuz, Graham C. Goodwin
      Pages 21-38
    4. Juan I. Yuz, Graham C. Goodwin
      Pages 39-45
    5. Juan I. Yuz, Graham C. Goodwin
      Pages 47-58
    6. Juan I. Yuz, Graham C. Goodwin
      Pages 59-71
    7. Juan I. Yuz, Graham C. Goodwin
      Pages 73-77
    8. Juan I. Yuz, Graham C. Goodwin
      Pages 79-99
    9. Juan I. Yuz, Graham C. Goodwin
      Pages 101-115
  4. Stochastic Systems

    1. Front Matter
      Pages 137-137
    2. Juan I. Yuz, Graham C. Goodwin
      Pages 139-147
    3. Juan I. Yuz, Graham C. Goodwin
      Pages 149-156
    4. Juan I. Yuz, Graham C. Goodwin
      Pages 157-167
    5. Juan I. Yuz, Graham C. Goodwin
      Pages 169-180
    6. Juan I. Yuz, Graham C. Goodwin
      Pages 181-193
    7. Juan I. Yuz, Graham C. Goodwin
      Pages 195-207
    8. Juan I. Yuz, Graham C. Goodwin
      Pages 209-220

About this book

Introduction

Sampled-data Models for Linear and Nonlinear Systems provides a fresh new look at a subject with which many researchers may think themselves familiar. Rather than emphasising the differences between sampled-data and continuous-time systems, the authors proceed from the premise that, with modern sampling rates being as high as they are, it is becoming more appropriate to emphasise connections and similarities. The text is driven by three motives:

·      the ubiquity of computers in modern control and signal-processing equipment means that sampling of systems that really evolve continuously is unavoidable;

·      although superficially straightforward, sampling can easily produce erroneous results when not treated properly; and

·      the need for a thorough understanding of many aspects of sampling among researchers and engineers dealing with applications to which they are central.

The authors tackle many misconceptions which, although appearing reasonable at first sight, are in fact either partially or completely erroneous. They also deal with linear and nonlinear, deterministic and stochastic cases. The impact of the ideas presented on several standard problems in signals and systems is illustrated using a number of applications.

Academic researchers and graduate students in systems, control and signal processing will find the ideas presented in Sampled-data Models for Linear and Nonlinear Systems to be a useful manual for dealing with sampled-data systems, clearing away mistaken ideas and bringing the subject thoroughly up to date. Researchers in statistics and economics will also derive benefit from the reworking of ideas relating a model derived from data sampling to an original continuous system.

 The Communications and Control Engineering series reports major technological advances which have potential for great impact in the fields of communication and control. It reflects research in industrial and academic institutions around the world so that the readership can exploit new possibilities as they become available.

Keywords

Data Sampling Discrete Models Nonlinear Systems Sampled-data Systems Stochastic Systems

Authors and affiliations

  • Juan I. Yuz
    • 1
  • Graham C. Goodwin
    • 2
  1. 1.Departamento de ElectrónicaUniversidad Técnica Federico Santa MaríaValparaísoChile
  2. 2.School of Electrical Engineering & Computer ScienceUniversity of NewcastleCallaghanAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-5562-1
  • Copyright Information Springer-Verlag London 2014
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-4471-5561-4
  • Online ISBN 978-1-4471-5562-1
  • Series Print ISSN 0178-5354
  • Series Online ISSN 2197-7119
  • About this book