A Novel Approach to Implement Takagi-Sugeno Fuzzy Models

Chia Wen Chang, Chin Wang Tao*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

35 Scopus citations

Abstract

This paper proposes new algorithms based on the fuzzy c-regressing model algorithm for Takagi-Sugeno (T-S) fuzzy modeling of the complex nonlinear systems. A fuzzy c-regression state model (FCRSM) algorithm is a T-S fuzzy model in which the functional antecedent and the state-space-model-type consequent are considered with the available input-output data. The antecedent and consequent forms of the proposed FCRSM consists mainly of two advantages: one is that the FCRSM has low computation load due to only one input variable is considered in the antecedent part; another is that the unknown system can be modeled to not only the polynomial form but also the state-space form. Moreover, the FCRSM can be extended to FCRSM-ND and FCRSM-Free algorithms. An algorithm FCRSM-ND is presented to find the T-S fuzzy state-space model of the nonlinear system when the input-output data cannot be precollected and an assumed effective controller is available. In the practical applications, the mathematical model of controller may be hard to be obtained. In this case, an online tuning algorithm, FCRSM-FREE, is designed such that the parameters of a T-S fuzzy controller and the T-S fuzzy state model of an unknown system can be online tuned simultaneously. Four numerical simulations are given to demonstrate the effectiveness of the proposed approach.

Original languageEnglish
Article number7929347
Pages (from-to)2353-2361
Number of pages9
JournalIEEE Transactions on Cybernetics
Volume47
Issue number9
DOIs
StatePublished - 09 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Fuzzy c-regression model (FCRM)
  • Takagi-Sugeno (T-S) fuzzy model

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