Optimization of fuzzy model using genetic algorithm for process control application

Abstract

A technique for the modeling of nonlinear controlprocesses using fuzzy modeling approach based on the Takagi–Sugeno fuzzymodel with a combination of geneticalgorithm and recursive least square is proposed. This paper discusses the identification of the parameters at the antecedent and consequent parts of the fuzzymodel. For the antecedent fuzzy parameters, geneticalgorithm is used to tune them while at the consequent part, recursive least squares approach is used to identify the system parameters. This approach is applied to a processcontrol rig with three subsystems: a heating element, a heat exchanger and a compartment tank. Experimental results show that the proposed approach provides better modeling when compared with Takagi Sugeno fuzzy modeling technique and the linear modeling approach.