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

Author

Listed:
  • Younes Zahraoui

    (FinEst Centre for Smart Cities, Tallinn University of Technology, Ehitajate Tee 5, 19086 Tallinn, Estonia)

  • Fardila M. Zaihidee

    (Faculty of Technical and Vocational, Sultan Idris Education University, Tanjong Malim 35900, Malaysia)

  • Mostefa Kermadi

    (Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Saad Mekhilef

    (Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
    School of Software and Electrical Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Victoria, VIC 3122, Australia
    Institute of Sustainable Energy, Universiti Tenaga Nasional (The National Energy University), Jalan Ikram-Uniten, Kajang 43000, Malaysia)

  • Marizan Mubin

    (Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Jing Rui Tang

    (Faculty of Technical and Vocational, Sultan Idris Education University, Tanjong Malim 35900, Malaysia)

  • Ezrinda M. Zaihidee

    (Centre for Mathematical Sciences, Universiti Malaysia Pahang, Gambang 26300, Malaysia)

Abstract

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.

Suggested Citation

  • Younes Zahraoui & Fardila M. Zaihidee & Mostefa Kermadi & Saad Mekhilef & Marizan Mubin & Jing Rui Tang & Ezrinda M. Zaihidee, 2023. "Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1457-:d:1099944
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    References listed on IDEAS

    as
    1. Fardila Mohd Zaihidee & Saad Mekhilef & Marizan Mubin, 2019. "Robust Speed Control of PMSM Using Sliding Mode Control (SMC)—A Review," Energies, MDPI, vol. 12(9), pages 1-27, May.
    2. Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
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    Cited by:

    1. Aydin Azizi & Mojtaba Naderi Soorki & Tahmineh Vedadi Moghaddam & Ali Soleimanizadeh, 2023. "A New Fractional-Order Adaptive Sliding-Mode Approach for Fast Finite-Time Control of Human Knee Joint Orthosis with Unknown Dynamic," Mathematics, MDPI, vol. 11(21), pages 1-16, November.
    2. Younes Zahraoui & Fardila M. Zaihidee & Mostefa Kermadi & Saad Mekhilef & Ibrahim Alhamrouni & Mehdi Seyedmahmoudian & Alex Stojcevski, 2023. "Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning," Energies, MDPI, vol. 16(11), pages 1-17, May.

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