Optimization of the Membership Functions for a Fuzzy Controller Using an Improved Genetic Algorithms

Abstract

Fuzzy control is a practical alternative for a variety of challenging control applications since it provides a convenient method for constructing nonlinear controllers via the use of heuristic information; this is done through the application of the techniques that use human judgment and experience rather than precise mathematical models. However, obtaining an optimal set of fuzzy membership functions (i.e. transferring the operator experience) is not an easy task. Different ways are suggested to deal with this problem. In this paper, we will use a powerful tool based on genetic algorithm to design a fuzzy logic controller that is used as a supervisor of PID controller that is the fuzzy controller is used to tune the PID controller for a feed water of a steam generation system. The simulation results show that the proposed technique is very useful to get an effective control action for the system.