Optimal design of fuzzy controllers using evolutionary genetic algorithms

The paper presents the applicability of evolutionary genetic algorithms (EGAs) in the optimal design of membership functions and sugeno rules for fuzzy logic controllers (FLCs). EGAs are fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human experts in the control design. The proposed technique is an optimization method that evaluates the fuzzy controller using the optimal response of the system. The potential of the method is examined using an inverted pendulum system. The membership functions and sugeno rules were optimized for initial fuzzy sets and sugeno parameters, respectively. The effectiveness of the proposed design control scheme and robustness of the obtained fuzzy controller is demonstrated through numeric simulations