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Author Campello, R.J.G.B. ♦ Amaral, W.C.
Sponsorship IEEE ♦ IEEE Neural Networks Soc
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2002
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Takagi-Sugeno model ♦ Fuzzy systems ♦ Fuzzy control ♦ System identification ♦ Control system synthesis ♦ Nonlinear control systems ♦ Nonlinear dynamical systems ♦ Topology ♦ Predictive control ♦ Predictive models
Abstract Fuzzy models within orthonormal basis function framework (OBF Fuzzy Models) have been introduced in previous works and shown to be a very promising approach to the areas of non-linear system identification and control since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. In the present paper, it is demonstrated that the OBF Takagi-Sugeno fuzzy models previously introduced by the authors are particular realizations of a more general and interpretable formulation presented here, while being able to approximate to desired accuracy a wide class of non-linear dynamic systems. In addition, a predictive control scheme based on the linearization of these models is applied to the control of a polymerization reactor.
Description Author affiliation: Dept. of Comput. Eng. & Ind. Autom., State Univ. of Campinas (Unicamp), Brazil (Campello, R.J.G.B.; Amaral, W.C.)
ISBN 0780372808
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2002-05-12
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 644.82 kB
Page Count 6
Starting Page 1399
Ending Page 1404

Source: IEEE Xplore Digital Library