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

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dc.citedby6
dc.contributor.authorZahraoui Y.en_US
dc.contributor.authorZaihidee F.M.en_US
dc.contributor.authorKermadi M.en_US
dc.contributor.authorMekhilef S.en_US
dc.contributor.authorMubin M.en_US
dc.contributor.authorTang J.R.en_US
dc.contributor.authorZaihidee E.M.en_US
dc.contributor.authorid57223913703en_US
dc.contributor.authorid56346969400en_US
dc.contributor.authorid57160269100en_US
dc.contributor.authorid57928298500en_US
dc.contributor.authorid25930079700en_US
dc.contributor.authorid56215182300en_US
dc.contributor.authorid54409895000en_US
dc.date.accessioned2024-10-14T03:18:43Z
dc.date.available2024-10-14T03:18:43Z
dc.date.issued2023
dc.description.abstractTracking 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1457
dc.identifier.doi10.3390/math11061457
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85151764574
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85151764574&doi=10.3390%2fmath11061457&partnerID=40&md5=84e531d2afb626e95bfb626040dc01ed
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34265
dc.identifier.volume11
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleMathematics
dc.subjectdisturbance estimation
dc.subjectmachine learning
dc.subjectmotor control
dc.subjectpermanent magnet synchronous motors
dc.subjectsliding mode control
dc.titleFractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSMen_US
dc.typeArticleen_US
dspace.entity.typePublication
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