ABSTRACT
Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets, denormalized gains and proper choice of rule base. There are several techniques reported in recent literature that use genetic algorithms to learn and optimize a fuzzy logic controller. This paper develops methodologies to learn and optimize fuzzy logic controller parameters based on genetic algorithm. The strategies developed have been applied to control integration between LQR and nonlinear Fuzzy PID of F16 aircraft pitch motion control and fuzzy controller developed with the help of iterative learning from operator experience. The results show that Genetic-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.