Analysis and design of uncertain fuzzy control systems - Part I : Fuzzy modelling and identification

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
Pages640-646
Volume1
Publication statusPublished - 1996
Externally publishedYes

Publication series

Name
Volume1

Conference

TitleProceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 1 (of 3)
CityNew Orleans, LA, USA
Period8 - 11 September 1996

Abstract

This paper is the first part of two papers dealing with the analysis and design of a class of fuzzy control systems with uncertainty and disturbance. This paper first analyzes the Mamdani and Takagi-Sugeno type fuzzy models which are widely used in the control area and argues that both of these fuzzy models cannot represent the uncertainties of a complex system. A new kind of dynamical fuzzy model called uncertain fuzzy model is proposed to represent a complex system which includes both linguistic information and system uncertainties. A new identification approach is then developed for the uncertain fuzzy model. Contrary to the prevailing LS methods, the final identification results are not parameters of a system model, but a feasible set of parameters which is consistent with the model structure, data and system uncertainties. The identification method is a kind of optimal recursive ellipsoid algorithm which is based on the famous Khachiyan ellipsoid algorithm in the context of linear programming.

Citation Format(s)

Analysis and design of uncertain fuzzy control systems - Part I: Fuzzy modelling and identification. / Cao, S. G.; Rees, N. W.; Feng, G.
IEEE International Conference on Fuzzy Systems. Vol. 1 1996. p. 640-646.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review