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BLDC Drive Control using Artificial Intelligence Technique

by Laxmiprasanna Ch, Ramesh Palakeerthi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 4
Year of Publication: 2015
Authors: Laxmiprasanna Ch, Ramesh Palakeerthi
10.5120/20731-3100

Laxmiprasanna Ch, Ramesh Palakeerthi . BLDC Drive Control using Artificial Intelligence Technique. International Journal of Computer Applications. 118, 4 ( May 2015), 5-9. DOI=10.5120/20731-3100

@article{ 10.5120/20731-3100,
author = { Laxmiprasanna Ch, Ramesh Palakeerthi },
title = { BLDC Drive Control using Artificial Intelligence Technique },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 4 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume118/number4/20731-3100/ },
doi = { 10.5120/20731-3100 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:00:45.858261+05:30
%A Laxmiprasanna Ch
%A Ramesh Palakeerthi
%T BLDC Drive Control using Artificial Intelligence Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 4
%P 5-9
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposed a control scheme of a neural network for the brushless direct current (BLDC) permanent magnet motor drives. The behavior of BLDC motor drive is nonlinear, cause it is complex to handle by using conventional proportional-integral (PI) controller. In order to overcome this main problem, artificial neural network controller technique is developed. The controller is intended to tracks variations of speed references and stabilizes the output speed during load variations. The mathematical model of BLDC motor and artificial neural network algorithm is derived. The effectiveness of the proposed method is established by developing simulation model in MATLAB/ Simulink. The simulation results show that the proposed Artificial neural network controller construct substantial improvement of the control performance compare to the PI controller for both condition controlling speed reference variations and load disturbance variations.

References
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Index Terms

Computer Science
Information Sciences

Keywords

BLDC Permanent Magnet PI controller ANN

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