Cover Image

Fractional order sliding mode controller based on supervised machine learning techniques for speed control of PMSM

Tracking the speed and current in permanent magnet synchronous motors (PMSMs) for industrial applications is challenging due to various external and internal disturbances such as parameter variations, unmodelled dynamics, and external load disturbances. Inaccurate tracking of speed and current resul...

Full description

Saved in:
Bibliographic Details
Main Authors: Zahraoui, Younes, Fardila, M. Zaihidee, Kermadi, Mostefa, Mekhilef, Saad, Marizan, Mubin, Tang, Jing Rui, Ezrinda, Mohd Zahidee
Format: Article
Language:English
Published: MDPI 2023
Subjects:
Q Science (General)
QA Mathematics
Online Access:http://umpir.ump.edu.my/id/eprint/37608/1/Fractional%20order%20sliding%20mode%20controller%20based%20on%20supervised%20machine%20learning%20techniques.pdf
http://umpir.ump.edu.my/id/eprint/37608/
https://doi.org/10.3390/math11061457
https://doi.org/10.3390/math11061457
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.37608
record_format eprints
spelling my.ump.umpir.376082023-07-06T02:56:09Z http://umpir.ump.edu.my/id/eprint/37608/ Fractional order sliding mode controller based on supervised machine learning techniques for speed control of PMSM Zahraoui, Younes Fardila, M. Zaihidee Kermadi, Mostefa Mekhilef, Saad Marizan, Mubin Tang, Jing Rui Ezrinda, Mohd Zahidee Q Science (General) QA Mathematics Tracking the speed and current in permanent magnet synchronous motors (PMSMs) for industrial applications is challenging due to various external and internal disturbances such as parameter variations, unmodelled dynamics, and external load disturbances. Inaccurate tracking of speed and current results in severe system deterioration and overheating. Therefore, the design of the controller for a PMSM is essential to ensure the system can operate efficiently under conditions of parametric uncertainties and significant variations. The present work proposes a PMSM speed controller using machine learning (ML) techniques for quick response and insensitivity to parameter changes and disturbances. The proposed ML controller is designed by learning fractional-order sliding mode control (FOSMC) controller behavior. The primary purpose of using ML in FOSMC is to avoid the self-tuning of the parameters and ensure the speed reaches the reference value in finite time with faster convergence and better tracking precision. Furthermore, the ML model does not require the mathematical model of the speed controller. In this work, several ML models are empirically evaluated on their estimation accuracy for speed tracking, namely ordinary least squares, passive-aggressive regression, random forest, and support vector machine. Finally, the proposed controller is implemented on a real-time hardware-in-the-loop (HIL) simulation platform from PLECS Inc. Comparative simulation and experimental results are presented and discussed. It is shown from the comparative study that the proposed FOSMC based on ML outperformed the traditional sliding mode control (SMC), which is more commonly used in industry in terms of tracking speed and accuracy. MDPI 2023-03 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/37608/1/Fractional%20order%20sliding%20mode%20controller%20based%20on%20supervised%20machine%20learning%20techniques.pdf Zahraoui, Younes and Fardila, M. Zaihidee and Kermadi, Mostefa and Mekhilef, Saad and Marizan, Mubin and Tang, Jing Rui and Ezrinda, Mohd Zahidee (2023) Fractional order sliding mode controller based on supervised machine learning techniques for speed control of PMSM. Mathematics, 11 (1457). pp. 1-21. ISSN 2227-7390. (Published) https://doi.org/10.3390/math11061457 https://doi.org/10.3390/math11061457
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
QA Mathematics
spellingShingle Q Science (General)
QA Mathematics
Zahraoui, Younes
Fardila, M. Zaihidee
Kermadi, Mostefa
Mekhilef, Saad
Marizan, Mubin
Tang, Jing Rui
Ezrinda, Mohd Zahidee
Fractional order sliding mode controller based on supervised machine learning techniques for speed control of PMSM
description Tracking the speed and current in permanent magnet synchronous motors (PMSMs) for industrial applications is challenging due to various external and internal disturbances such as parameter variations, unmodelled dynamics, and external load disturbances. Inaccurate tracking of speed and current results in severe system deterioration and overheating. Therefore, the design of the controller for a PMSM is essential to ensure the system can operate efficiently under conditions of parametric uncertainties and significant variations. The present work proposes a PMSM speed controller using machine learning (ML) techniques for quick response and insensitivity to parameter changes and disturbances. The proposed ML controller is designed by learning fractional-order sliding mode control (FOSMC) controller behavior. The primary purpose of using ML in FOSMC is to avoid the self-tuning of the parameters and ensure the speed reaches the reference value in finite time with faster convergence and better tracking precision. Furthermore, the ML model does not require the mathematical model of the speed controller. In this work, several ML models are empirically evaluated on their estimation accuracy for speed tracking, namely ordinary least squares, passive-aggressive regression, random forest, and support vector machine. Finally, the proposed controller is implemented on a real-time hardware-in-the-loop (HIL) simulation platform from PLECS Inc. Comparative simulation and experimental results are presented and discussed. It is shown from the comparative study that the proposed FOSMC based on ML outperformed the traditional sliding mode control (SMC), which is more commonly used in industry in terms of tracking speed and accuracy.
format Article
author Zahraoui, Younes
Fardila, M. Zaihidee
Kermadi, Mostefa
Mekhilef, Saad
Marizan, Mubin
Tang, Jing Rui
Ezrinda, Mohd Zahidee
author_facet Zahraoui, Younes
Fardila, M. Zaihidee
Kermadi, Mostefa
Mekhilef, Saad
Marizan, Mubin
Tang, Jing Rui
Ezrinda, Mohd Zahidee
author_sort Zahraoui, Younes
title Fractional order sliding mode controller based on supervised machine learning techniques for speed control of PMSM
title_short Fractional order sliding mode controller based on supervised machine learning techniques for speed control of PMSM
title_full Fractional order sliding mode controller based on supervised machine learning techniques for speed control of PMSM
title_fullStr Fractional order sliding mode controller based on supervised machine learning techniques for speed control of PMSM
title_full_unstemmed Fractional order sliding mode controller based on supervised machine learning techniques for speed control of PMSM
title_sort fractional order sliding mode controller based on supervised machine learning techniques for speed control of pmsm
publisher MDPI
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/37608/1/Fractional%20order%20sliding%20mode%20controller%20based%20on%20supervised%20machine%20learning%20techniques.pdf
http://umpir.ump.edu.my/id/eprint/37608/
https://doi.org/10.3390/math11061457
https://doi.org/10.3390/math11061457
_version_ 1822923463090765824
score 13.244369

Similar Items