Genetic algorithm-based self-learning fuzzy PI controller for buck converter

T. L. Liao, N. S. Huang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


This paper presents a self-learning fuzzy proportional-integral (PI) controller design for a buck converter. Although a triangle type of membership functions is used, their parameters for fuzzy subsets are deemed unnecessary 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 results 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.

Original languageEnglish
Pages (from-to)233-239
Number of pages7
JournalEuropean Transactions on Electrical Power
Issue number4
Publication statusPublished - 1999 Jan 1

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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