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T-S fuzzy model identification and the fuzzy model based controller design

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2 Author(s)
Chung-Chun Kung ; Tatung Univ., Taipei ; Jui-Yiao Su

This paper presents an algorithm to identify T-S fuzzy models and design fuzzy model based controllers (FMBC) for a class of nonlinear plant. First, the algorithm using fuzzy c-regression models (FCRM) clustering to find the functional relationships in the product space of the input-output data. A new cluster validity criterion is proposed to calculate overall compactness and separateness of the FCRM partition and then determine the appropriate number of regression models. Besides, the fine-tuning of the antecedent fuzzy set and the consequent parameters are considered. Thus, an efficient T-S fuzzy model with compact if-then rules can be generated systematically. Finally, an FMBC is proposed to make the nonlinear plant track the reference trajectory signal. A simulation example is provided to demonstrate the accuracy and effectiveness of the proposed algorithm.

Published in:

Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on

Date of Conference:

7-10 Oct. 2007