Fuzzy logic, identification and predictive control.
type: Item
instance of: scholarly article
title: Fuzzy logic, identification and predictive control.
zbMATH Open document ID: 1061.93001
author: Jairo Espinosa
author: Joos Vandewalle
author: Vincent Wertz
published in: Advances in Industrial Control
publication date: 2005-02-11T00:00:00Z
review text: New insights into the transfer of fuzzy methods into the modern control paradigms encompassing robust, model-based, PID-like, and predictive control are presented in this book. The presentation is balanced between fuzzy models (Ch.~1--4) and fuzzy control (Ch.~5--8). The approximation capabilities of triangular, polynomial, and Gaussian types of membership functions are analyzed. It is shown that any continuous function can be approximated with an arbitrary accuracy by a fuzzy inference system on a compact domain (Ch.~1). Main techniques to construct fuzzy models from input-output data are recalled and compared. The problems of generalization and consequence estimation are outlined. It is stated that the fuzzy model will be at least as good as the best multi-linear approximation (Ch.~2). The concept of linguistic integrity is introduced, and an algorithm to build up fuzzy models with linguistic meaning is proposed. Fuzzy models of complex nonlinear functions, chaotic time series, and industrial processes are elaborated using this model-building tool (Ch.~3). A framework for identification of nonlinear dynamic systems using fuzzy models is developed. The most suitable techniques which can be used in each construction phase (experiment design, system identification, selection of regressors, structure and parameter adjusting, and validation) are treated in detail (Ch.~4). The most popular fuzzy controller design techniques (model-free, adaptive model-based, fuzzy gain scheduling) are reviewed and analyzed. It is proved that a fuzzy controller will guarantee the same or better performance than the one provided by a PID controller (Ch.~5). Unconstrained and constrained fuzzy predictive control strategies based on the combination of fuzzy modeling and model predictive control are proposed. The key point of the proposed strategies is the reduction of an original complex nonlinear program to a simple quadratic program by exploiting specific structure of fuzzy models (Ch.~6)…
reviewed by: Ingmar Randvee
Mathematics Subject Classification ID: 93-02
Mathematics Subject Classification ID: 93C42
Mathematics Subject Classification ID: 94D05
Mathematics Subject Classification ID: 93B51
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 93A30
Mathematics Subject Classification ID: 90C20
Mathematics Subject Classification ID: 93C40
zbMATH DE Number: 2133308
zbMATH Keywords: fuzzy models
zbMATH Keywords: fuzzy control systems
zbMATH Keywords: data mining
zbMATH Keywords: predictive control
zbMATH Keywords: PID
zbMATH Keywords: linguistic integrity
zbMATH Keywords: identification
zbMATH Keywords: gain scheduling
zbMATH Keywords: quadratic program
zbMATH Keywords: Takagi-Sugeno fuzzy model
describes a project that uses: Daisy
describes a project that uses: Genocop
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 2133308