T-S fuzzy model identification and the fuzzy model based controller design
Chung‐Chun Kung, Jui-Yiao Su · 2007
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.