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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Information Sciencesarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Information Sciences
Article . 2012 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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On the accessibility/controllability of fuzzy control systems

Authors: William Melek; Mohammad Biglarbegian; Alireza Sadeghian;

On the accessibility/controllability of fuzzy control systems

Abstract

Accessibility/controllability are very important concepts in the design of fuzzy logic control systems (FLCS) from both theoretical and practical perspectives. This paper proposes a systematic methodology that utilizes differential geometry/Lie Algebra, that has not been investigated in the literature, for analyzing the accessibility/controllability of TS FLCS. First, we linearize TS FLCS and show that this process, although it is straightforward, has limitations when analyzing some FLCS structures. Thus, we propose a novel algorithm by consolidating the linearization and Lie Algebra techniques that provides a systematic approach to assess accessibility/controllability. The advantage of the proposed algorithm is that in the case of nonaccessibility, those associated trajectories are found. The proposed approach is generic and does not require assumptions on FLCS's rules or structures as is common with the few existing fuzzy systems controllability analysis methods in the literature. Moreover, we show that unlike exiting methods, our approach is not limited to small rule bases, but is also applicable for systems with large number of rules. To validate the effectiveness of the proposed method, we present several illustrative benchmark examples that will be of practical use to control engineers and can, as well, enable wider scale use of FLCS in various engineering applications.

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    11
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Average
Top 10%
Average
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