Fuzzy Logic, Identification and Predictive Control

  • Jairo Espinosa
  • Joos Vandewalle
  • Vincent Wertz

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

Table of contents

About this book

Introduction

The complexity and sensitivity of modern industrial processes and systems increasingly require adaptable advanced control protocols. These controllers have to be able to deal with circumstances demanding "judgement" rather than simple "yes/no", "on/off" responses, circumstances where an imprecise linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious in this form of expert control system.

Divided into two parts, Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real-world industrial systems and simulations. The second part demonstrates the exploitation of such models to design control systems employing techniques like data mining.

Fuzzy Logic, Identification and Predictive Control is a comprehensive introduction to the use of fuzzy methods in many different control paradigms encompassing robust, model-based, PID-like and predictive control. This combination of fuzzy control theory and industrial serviceability will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Keywords

Control Control Applications Data Mining Fuzzy Control Fuzzy Modelling Intelligent Control Nonlinear Predictive Control control engineering modeling simulation

Authors and affiliations

  • Jairo Espinosa
    • 1
  • Joos Vandewalle
    • 2
  • Vincent Wertz
    • 3
  1. 1.IPCOS BelgiumHeverlee (Leuven)Belgium
  2. 2.Faculty of Engineering, KU LeuvenESATHeverlee (Leuven)Belgium
  3. 3.Université Catholique de LouvainCESAMELouvain-la-NeuveBelgium

Bibliographic information

  • DOI https://doi.org/10.1007/b138626
  • Copyright Information Springer-Verlag London Limited 2005
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-85233-828-2
  • Online ISBN 978-1-84628-087-0
  • Series Print ISSN 1430-9491
  • Series Online ISSN 2193-1577
  • About this book