Deterministic and Stochastic Time-Delay Systems

  • El-Kébir Boukas
  • Zi-Kuan Liu

Part of the Control Engineering book series (CONTRENGIN)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. El-Kébir Boukas, Zi-Kuan Liu
    Pages 1-17
  3. El-Kébir Boukas, Zi-Kuan Liu
    Pages 21-29
  4. El-Kébir Boukas, Zi-Kuan Liu
    Pages 31-67
  5. El-Kébir Boukas, Zi-Kuan Liu
    Pages 69-98
  6. El-Kébir Boukas, Zi-Kuan Liu
    Pages 99-130
  7. El-Kébir Boukas, Zi-Kuan Liu
    Pages 131-173
  8. El-Kébir Boukas, Zi-Kuan Liu
    Pages 177-183
  9. El-Kébir Boukas, Zi-Kuan Liu
    Pages 185-228
  10. El-Kébir Boukas, Zi-Kuan Liu
    Pages 361-376
  11. Back Matter
    Pages 377-423

About this book

Introduction

Most practical processes such as chemical reactor, industrial furnace, heat exchanger, etc., are nonlinear stochastic systems, which makes their con­ trol in general a hard problem. Currently, there is no successful design method for this class of systems in the literature. One common alterna­ tive consists of linearizing the nonlinear dynamical stochastic system in the neighborhood of an operating point and then using the techniques for linear systems to design the controller. The resulting model is in general an approximation of the real behavior of a dynamical system. The inclusion of the uncertainties in the model is therefore necessary and will certainly improve the performance of the dynamical system we want to control. The control of uncertain systems has attracted a lot of researchers from the control community. This topic has in fact dominated the research effort of the control community during the last two decades, and many contributions have been reported in the literature. Some practical dynamical systems have time delay in their dynamics, which makes their control a complicated task even in the deterministic case. Recently, the class ofuncertain dynamical deterministic systems with time delay has attracted some researchers, and some interesting results have been reported in both deterministic and stochastic cases. But wecan't claim that the control problem ofthis class ofsystems is completely solved; more work must be done for this class of systems.

Keywords

Markov Markov process algorithm control engineering dynamical systems filtering problem stochastics

Authors and affiliations

  • El-Kébir Boukas
    • 1
  • Zi-Kuan Liu
    • 1
  1. 1.Mechanical Engineering DepartmentÉcole Polytechnique de MontrealMontrealCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-0077-2
  • Copyright Information Birkhäuser Boston 2002
  • Publisher Name Birkhäuser Boston
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-8176-4245-7
  • Online ISBN 978-1-4612-0077-2
  • About this book