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Predictive Approaches to Control of Complex Systems

  • Gorazd Karer
  • Igor Škrjanc

Part of the Studies in Computational Intelligence book series (SCI, volume 454)

Table of contents

  1. Front Matter
    Pages 1-10
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Gorazd Karer, Igor Škrjanc
      Pages 3-8
  3. Modeling of Complex Systems for Predictive Control

    1. Front Matter
      Pages 9-9
    2. Gorazd Karer, Igor Škrjanc
      Pages 11-21
    3. Gorazd Karer, Igor Škrjanc
      Pages 23-32
    4. Gorazd Karer, Igor Škrjanc
      Pages 33-47
  4. Modeling an Identification of a Batch Reactor

    1. Front Matter
      Pages 99-99
    2. Gorazd Karer, Igor Škrjanc
      Pages 101-104
    3. Gorazd Karer, Igor Škrjanc
      Pages 105-130
    4. Gorazd Karer, Igor Škrjanc
      Pages 131-144
  5. Predictive Control of Complex Systems

    1. Front Matter
      Pages 145-145
    2. Gorazd Karer, Igor Škrjanc
      Pages 147-156
    3. Gorazd Karer, Igor Škrjanc
      Pages 157-168
    4. Gorazd Karer, Igor Škrjanc
      Pages 169-191
    5. Gorazd Karer, Igor Škrjanc
      Pages 193-214
    6. Gorazd Karer, Igor Škrjanc
      Pages 235-252
  6. Conclusion

    1. Front Matter
      Pages 253-253
    2. Gorazd Karer, Igor Škrjanc
      Pages 255-256
  7. Back Matter
    Pages 0--1

About this book

Introduction

A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm.

This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems.

Keywords

Adaptive Control Complex Dynamics Fuzzy Systems Genetic Algorithms Hybrid Systems Identification Modelling Multivariable Systems Nonlinear Systems Optimization Predictive Control Time-delayed Systems Time-varying Systems Unstable Systems

Authors and affiliations

  • Gorazd Karer
    • 1
  • Igor Škrjanc
    • 2
  1. 1., Faculty of Electrical EngineeringUniversity of LjubljanaLjubljanaSlovenia
  2. 2., Faculty of Electrical EngineeringUniversity of LjubljanaLjubljanaSlovenia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-33947-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-33946-2
  • Online ISBN 978-3-642-33947-9
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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