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131680

Adaptive Fuzzy Logic Controller for DC-DC Converters.

Article

Last updated: 22 Jan 2023

Subjects

-

Tags

Electrical Engineering

Abstract

Two adaptive fuzzy logic controller (AFLC) topologies for the DC-DC converter are developed and presented in this paper. They essentially consist of combining fuzzy inference system and neural networks and implementing them within the framework of adaptive networks. The architecture of the AFLC along with the learning rule, which is used to give an adaptive and learning structure to a fuzzy controller, is also described. The emphasis here is on fuzzy-neural-network control philosophies in designing a novel controller for the DC-DC converter that allows the benefits of neural network structure to be realized without sacrificing the intuitive nature of fuzzy system. The AFLC topologies are built on Matlab environment and tested for both the buck and buck boost converter for load regulation and line regulation. The proposed AFLCs have satisfactory results for tracking the reference output voltage. 

DOI

10.21608/bfemu.2020.131680

Authors

First Name

Sahar Sidky

Last Name

Kaddah

MiddleName

El-Hefni

Affiliation

Electrical Power& Machines Department., El-Mansours University., Mansoura., Egypt

Email

-

City

Mansoura

Orcid

-

First Name

Ahmed

Last Name

Rubaai

MiddleName

-

Affiliation

Electrical Engineering Department, lloward University, Washington, DC USA

Email

-

City

-

Orcid

-

Volume

30

Article Issue

3

Related Issue

19436

Issue Date

2005-09-01

Receive Date

2005-05-11

Publish Date

2020-12-22

Page Start

22

Page End

28

Print ISSN

1110-0923

Online ISSN

2735-4202

Link

https://bfemu.journals.ekb.eg/article_131680.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=131680

Order

9

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023