Optimal Design of Type_1 TSK Fuzzy Controller Using GRLA

A new methodology for designing optimal systematic GA-based fuzzy controller is presented in this paper. Our design is based on Genetic Reinforcement Learning Algorithm (GRLA), unlike conventional GA that is based on the competition between chromosomes only to survive, this method is based on competition and cooperation between chromosomes, GA tries to find good chromosomes and good combination for them to form an optimal fuzzy controller. The proposed GRLA design method has been applied to the cart-pole balancing system. The controller was capable of balancing the pole for initial conditions up to 80°. As a comparison we applied a Mamdani controller which is designed through normal GA and uses five membership functions for inputs and output variables to the same problem. the results show the efficiency of the proposed method.

کلیدواژه ها:

Genetic Reinforcement Learning Algorithm (GRLA) ، TSK Fuzzy Controller