Providing a genetic algorithm-based method to optimize the fuzzy logic controller for the inverted pendulum

A classic system in dynamics and control is the inverted pendulum, which is known as a topic in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, to find the optimal fuzzy rule-based system, different approaches have been developed using a genetic algorithm. The proposed method's purpose is to set fuzzy rules and their membership function and the learning process length based on the use of a genetic algorithm. The proposed method's results show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. The use of a fuzzy system in a dynamic inverted pendulum environment has better results than definite systems; in addition, the optimization of the control parameters increases this model quality even beyond the simple case.