Optimal design for fuzzy controllers by genetic algorithms

Fuzzy control has been applied to various industrial processes; however, its control rules and membership functions are usually obtained by trial and error. Proposed in this paper is an optimal design for membership functions and control rules simultaneously by a genetic algorithm (GA). GAs are search algorithms based on the mechanics of natural selection and natural genetics. They are easy to implement and efficient for multivariable optimization problems, such as fuzzy controller design. The simulation result shows that the fuzzy controller thus designed can achieve good performance merely by using a few fuzzy variables.