60074

A CONJUGATE GRADIENT TECHNIQUE FOR OPTIMAL FUZZY LOGIC CONTROL OF HIGHLY COMPLEX DYNAMIC SYSTEM

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Last updated: 04 Jan 2025

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Abstract

The paper presents a novel approach for the application of adaptive fuzzy logic control' algorithm. The aim of this adaptive system is to minimize an objective function in the output error via varying the center of each label. The model inependency characteristics of the fuzzy controller allows simple calculation and low implementation cost which are highly recommended features in control industry. A conjugate gradient technique is applied in this paper to inqiiinize the error in least square sense. A real time nonlinear dynamic system is used to ensure the effectiveness of tAe proposed control scheme.

DOI

10.21608/iceeng.1998.60074

Keywords

Conjugate gradient technique, Optimal fuzzy logic control'

Authors

First Name

I.

Last Name

Khalifa

MiddleName

H.

Affiliation

Prof, Dept. of Elect. Machines and Power Engineering, Helwan University.

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First Name

A.

Last Name

El-Assal

MiddleName

H.

Affiliation

Assistant Prof., Dept. of Elect. Machines and Power Engineering, Helwan University.

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-

City

-

Orcid

-

First Name

M.

Last Name

Saleh

MiddleName

H.

Affiliation

Engineer, Maintainance Head. Faculty of Engineering at Helwan. Helwan University.

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-

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-

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Volume

1

Article Issue

1st International Conference on Electrical Engineering ICEENG 1998

Related Issue

9113

Issue Date

1998-03-01

Receive Date

2019-11-18

Publish Date

1998-03-01

Page Start

39

Page End

46

Print ISSN

2636-4433

Online ISSN

2636-4441

Link

https://iceeng.journals.ekb.eg/article_60074.html

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https://iceeng.journals.ekb.eg/service?article_code=60074

Order

5

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Original Article

Type Code

833

Publication Type

Journal

Publication Title

The International Conference on Electrical Engineering

Publication Link

https://iceeng.journals.ekb.eg/

MainTitle

A CONJUGATE GRADIENT TECHNIQUE FOR OPTIMAL FUZZY LOGIC CONTROL OF HIGHLY COMPLEX DYNAMIC SYSTEM

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Article

Created At

22 Jan 2023