Staff View: Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM

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...

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Main Authors:	Zahraoui Y., Zaihidee F.M., Kermadi M., Mekhilef S., Mubin M., Tang J.R., Zaihidee E.M.
Other Authors:	57223913703
Format:	Article
Published:	MDPI 2024
Subjects:	disturbance estimation machine learning motor control permanent magnet synchronous motors sliding mode control
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id	my.uniten.dspace-34265
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spelling	my.uniten.dspace-342652024-10-14T11:18:43Z Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM Zahraoui Y. Zaihidee F.M. Kermadi M. Mekhilef S. Mubin M. Tang J.R. Zaihidee E.M. 57223913703 56346969400 57160269100 57928298500 25930079700 56215182300 54409895000 disturbance estimation machine learning motor control permanent magnet synchronous motors sliding mode control 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. � 2023 by the authors. Final 2024-10-14T03:18:43Z 2024-10-14T03:18:43Z 2023 Article 10.3390/math11061457 2-s2.0-85151764574 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151764574&doi=10.3390%2fmath11061457&partnerID=40&md5=84e531d2afb626e95bfb626040dc01ed https://irepository.uniten.edu.my/handle/123456789/34265 11 6 1457 All Open Access Gold Open Access MDPI Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	disturbance estimation machine learning motor control permanent magnet synchronous motors sliding mode control
spellingShingle	disturbance estimation machine learning motor control permanent magnet synchronous motors sliding mode control Zahraoui Y. Zaihidee F.M. Kermadi M. Mekhilef S. Mubin M. Tang J.R. Zaihidee E.M. 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. � 2023 by the authors.
author2	57223913703
author_facet	57223913703 Zahraoui Y. Zaihidee F.M. Kermadi M. Mekhilef S. Mubin M. Tang J.R. Zaihidee E.M.
format	Article
author	Zahraoui Y. Zaihidee F.M. Kermadi M. Mekhilef S. Mubin M. Tang J.R. Zaihidee E.M.
author_sort	Zahraoui Y.
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	2024
_version_	1814061113069797376
score	13.222552

Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM

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