Genetic algorithm for the design of a class of fuzzy controllers: an alternative approach

Abstract

A simple, easy to implement alternative method for designing fuzzy logic controllers (FLCs) with symmetrically distributed fuzzy sets in a universe of discourse is introduced. The design parameters include the parameters of the membership functions of the inputs and outputs and the rule base. The method is based on a network implementation of the FLC with real and binary weights with constraints. Due to the presence of the binary weights the backpropagation technique cannot be used. The learning problem is cast as a mixed integer constrained dynamic optimization problem and solved using the genetic algorithm (GA). The crossover and mutation are slightly disrupted in order to cope with the constraints on the binary weights. Training of the controller is carried out in a closed-loop simulation with the controller in the loop.