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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 33, Number 6(2) (2021)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 1945-1955
S&M2585 Research Paper of Special Issue https://doi.org/10.18494/SAM.2021.3271 Published: June 9, 2021 Field-oriented Controlled Permanent Magnet Synchronous Motor Drive with Dynamic-parameter Speed Controller Based on Generalized Regression Neural Network [PDF] Yung-Chang Luo, Hsu-Hung Zheng, Chia-Hung Lin, and Ying-Piao Kuo (Received December 29, 2020; Accepted March 5, 2021) Keywords: dynamic control parameters, field-oriented controlled (FOC), permanent magnet synchronous motor (PMSM) drive, generalized regression neural network (GRNN), firefly algorithm (FA)
A dynamic-parameter speed controller based on a generalized regression neural network (GRNN) was developed for a field-oriented controlled (FOC) permanent magnet synchronous motor (PMSM) drive. The decoupled FOC PMSM drive was established using the current and voltage of the stator. The designed time-varying-parameters speed controller replaced the conventional fixed-parameters speed controller to adapt to drastic load variations and serious interference. A GRNN was utilized to develop the time-varying-parameters speed controller, and the smooth curve of the pattern layer was adjusted using the firefly algorithm (FA). Hall effect current sensors were used as an electromagnetic sensing element to detect the stator current from the PMSM. The MATLAB/Simulink© toolbox was used to establish the simulation scheme, and all the control algorithms were realized using a TI DSP 6713-and-F2812 control card. Simulation and experimental results under load changes confirmed the effectiveness of the proposed approach.
Corresponding author: Yung-Chang Luo  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Yung-Chang Luo, Hsu-Hung Zheng, Chia-Hung Lin, and Ying-Piao Kuo, Field-oriented Controlled Permanent Magnet Synchronous Motor Drive with Dynamic-parameter Speed Controller Based on Generalized Regression Neural Network, Sens. Mater., Vol. 33, No. 6, 2021, p. 1945-1955. |
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