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

Teh-Lu Liao, N. S. Huang

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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
Volume9
Issue number4
DOIs
Publication statusPublished - 1999 Jan 1

Fingerprint

Buck Converter
Self-learning
Genetic algorithms
Directly proportional
Voltage
Genetic Algorithm
Controller
Membership Function
Controllers
Normalization
Membership functions
Fuzzy Subset
Electric potential
Design Rules
Output
Fuzzy Controller
Fuzzy Control
Controller Design
Optimization Techniques
Closed-loop System

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

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Genetic algorithm-based self-learning fuzzy PI controller for buck converter. / Liao, Teh-Lu; Huang, N. S.

In: European Transactions on Electrical Power, Vol. 9, No. 4, 01.01.1999, p. 233-239.

Research output: Contribution to journalArticle

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