Genetic Algorithm-Based Self-Leaning Fuzzy PI Controller for Buck Converter

摘要

This paper presents a self learning fuzzy proportional-integral(PI) controller design for a buck converter. Al- though a triangle type of membership functions is used, their parameters for fuzzy subsets are deemed unnec- essary in the fuzzy, control rules design. Knowledge of both the normalization factor in the fuzzification phase and feedback gain of the PI controller is also not required. In addition, a genetic algorithm (GA) optimization technique is proposed to tune the parameters of normalization factors, membership functions and the gain of PI-like controller The proposed GA-based fuzzy controller is then applied to a buck converter simulation re- suits indicate that the output voltage of the closed-loop system can be regulated to a desired reference voltage regardless of the variations in input voltage and of changes in output load.