Multivariable GA-Based Identification of TS Fuzzy Models: MIMO Distillation Column Model Case Study

In this paper, a nonlinear fuzzy identification approach based on genetic algorithm (GA) and Takagi-Sugeno (TS) fuzzy system is presented for fuzzy modeling of a multi-input, multi-output (MIMO) dynamical system. In this approach, GA is used for tuning the parameters of the membership functions of the antecedent parts of IF-THEN rules and Recursive Least-Squares (RLS) algorithm is employed for parameter estimation of the consequent linear sub-model parts of the TS fuzzy rules. The presented method is implemented on a simulated nonlinear MIMO distillation column. The results show that the presented method gives a more accurate model in comparison with the conventional TS fuzzy identification approach.