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Journal ArticleDOI

Fuzzy regulators and fuzzy observers: relaxed stability conditions and LMI-based designs

TLDR
New relaxed stability conditions and LMI- (linear matrix inequality) based designs for both continuous and discrete fuzzy control systems are applied to design problems of fuzzy regulators and fuzzy observers.
Abstract
This paper presents new relaxed stability conditions and LMI- (linear matrix inequality) based designs for both continuous and discrete fuzzy control systems. They are applied to design problems of fuzzy regulators and fuzzy observers. First, Takagi and Sugeno's fuzzy models and some stability results are recalled. To design fuzzy regulators and fuzzy observers, nonlinear systems are represented by Takagi-Sugeno's (TS) fuzzy models. The concept of parallel distributed compensation is employed to design fuzzy regulators and fuzzy observers from the TS fuzzy models. New stability conditions are obtained by relaxing the stability conditions derived in previous papers, LMI-based design procedures for fuzzy regulators and fuzzy observers are constructed using the parallel distributed compensation and the relaxed stability conditions. Other LMI's with respect to decay rate and constraints on control input and output are also derived and utilized in the design procedures. Design examples for nonlinear systems demonstrate the utility of the relaxed stability conditions and the LMI-based design procedures.

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Journal ArticleDOI

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

TL;DR: A survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models.
Journal ArticleDOI

Parameterized linear matrix inequality techniques in fuzzy control system design

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Survey of Gain-Scheduling Analysis & Design

TL;DR: The scope of this paper includes the main theoretical results and design procedures relating to continuous gain-scheduling (in the sense of decomposition of non-linear design into linear sub-problems) control with the aim of providing both a critical overview and a useful entry point into the relevant literature.
References
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Journal ArticleDOI

Fuzzy identification of systems and its applications to modeling and control

TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
Journal ArticleDOI

An approach to fuzzy control of nonlinear systems: stability and design issues

TL;DR: The authors represent a nonlinear plant with a Takagi-Sugeno fuzzy model with a model-based fuzzy controller design utilizing the concept of the so-called "parallel distributed compensation" and presents a design methodology for stabilization of a class of nonlinear systems.
Journal ArticleDOI

Stability analysis and design of fuzzy control systems

TL;DR: The fuzzy block diagrams and the stability analysis are applied to the design problems of a model-based fuzzy controller and a new design technique of a fuzzy controller is proposed.
Book

Fuzzy Control

TL;DR: Drawing on their extensive experience working with industry on implementations, Kevin Passino and Stephen Yurkovich have written an excellent hands-on introduction for professionals and educators interested in learning or teaching fuzzy control.