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Fuzzy logic, identification, and predictive control / Jairo Espinosa, Joos Vandewalle, Vincent Wertz.

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Bibliographic Details
Online Access: Full Text (via Springer)
Main Author: Espinosa, Jairo
Other Authors: Vandewalle, J. (Joos), 1948-, Wertz, Vincent
Format: eBook
Language:English
Published: London ; New York : Springer, 2004.
Series:Advances in industrial control.
Subjects:
Predictive control.
Fuzzy systems.
Table of Contents:
  • Cover
  • Preface
  • Table of Contents
  • Part I Fuzzy Modeling
  • 1 Fuzzy Modeling.
  • 1.1 Function Approximation .
  • 1.2 Approximation Capabilities of Takagi ... Sugeno Fuzzy Models
  • 1.3 Conclusion and Summary
  • 2 Constructing Fuzzy Models from Input-Output Data
  • 2.1 Mosaic or Table Lookup Scheme.
  • 2.2 Using Gradient Descent
  • 2.3 Using Clustering and Gradient Descent
  • 2.4 Using Evolutionary Strategies
  • 2.5 Generalization and Consequences Estimation.
  • 2.6 Example of an Industrial Application
  • 2.7 Conclusions
  • 3 Fuzzy Modeling with Linguistic Integrity: A Tool for Data Mining
  • 3.1 Introduction
  • 3.2 Structure of the Fuzzy Model
  • 3.3 The AFRELI Algorithm.
  • 3.4 The FuZion Algorithm
  • 3.5 Examples.
  • 3.6 Complexity of the AFRELI Algorithm
  • 3.7 Conclusions
  • 4 Nonlinear Identification Using Fuzzy Models
  • 4.1 System Identification
  • 4.2 Basic Structure of the Fuzzy System
  • 4.3 Experiment Design for System Identification
  • 4.4 Choosing the Regressors
  • 4.5 Choosing the Structure.
  • 4.6 Calculating the Parameters
  • 4.7 Validation
  • 4.8 Example: Identification of the Box and Jenkins Gas Furnace Data Set
  • 4.9 Identification of Takagi ... Sugeno Fuzzy Models Using Local Linear Identification
  • 4.10 Conclusions
  • Part II Fuzzy Control
  • 5 Fuzzy Control
  • 5.1 Model-Free Fuzzy Control
  • 5.2 Model Based Fuzzy Control
  • 5.3 Conclusions and Future Perspectives
  • 6 Predictive Control Based on Fuzzy Models
  • 6.1 The Predictive Control Strategy
  • 6.2 Unconstrained Nonlinear Predictive Control
  • 6.3 Constrained Nonlinear Predictive Control
  • 6.4 Conclusions
  • 7 Robust Nonlinear Predictive Control Using Fuzzy Models
  • 7.1 Introduction
  • 7.2 Robust Quadratic Programming
  • 7.3 Problem Description
  • 7.4 Nominal Solution
  • 7.5 Formulation of the MPC Problem as a Robust QP.
  • 7.6 The Control Algorithm.
  • 7.7 Uncertainty Description in Fuzzy Models
  • 7.8 Conclusions and Perspectives
  • 8 Conclusions and Future Perspectives
  • 8.1 Conclusions and Summary
  • 8.2 Perspectives and Future Work.
  • Part III Appendices
  • A Fuzzy Set Theory
  • B Clustering Methods
  • C Gradients Used in Identification with Fuzzy Models
  • D Discrete Linear Dynamical System Approximation Theorem.
  • E Fuzzy Control for a Continuously Variable Transmission
  • References.

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