IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i11p4353-d1156962.html
   My bibliography  Save this article

Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning

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, Selangor, Malaysia)

  • Ibrahim Alhamrouni

    (Department of EEE, Universiti Kuala Lumpur, British Malaysian Institute, 8, Jalan Sungai Pusu, Gombak 53100, Selangor, Malaysia)

  • Mehdi Seyedmahmoudian

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

  • Alex Stojcevski

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

Abstract

An improved fractional-order sliding mode control (FOSMC) for PMSM is presented in this study to set the unavoidable parameters and to improve permanent magnet synchronous motors (PMSMs) drive performance, such as current and speed tracking accuracy. To determine the optimal parameters of the FOSMC for control speed in a PMSM drive, a neural network algorithm with reinforcement learning (RLNNA) is proposed. The FOSMC parameters are set by the ANN algorithm and then adapted through reinforcement learning to enhance the results. The proposed controller using RLNNA based on fractional-order sliding mode control (RLNNA-FOSMC) can drive the motor speed to achieve the referred value in a finite period of time, leading to faster convergence and improved tracking accuracy. For a fair comparison and evaluation, the proposed RLNNA-FOSMC is compared with conventional FOSMC by applying the integral of time multiplied absolute error as an objective function. The most commonly used objective functions in the literature were also compared, including the integral time multiplied square error, integral square error, and integral absolute error. To validate the performance of the RLNNA-FOSMC speed controller, different scenarios with different speeds steps were carried out. The computational results are promising and demonstrate the effectiveness of the proposed controller. Overall, the proposed RLNNA-FOSMC controller for the PMSM speed control system performed better than conventional FOSMC in numerical simulations.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4353-:d:1156962
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/11/4353/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/11/4353/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Peng Gao & Guangming Zhang & Xiaodong Lv, 2021. "Model-Free Control Using Improved Smoothing Extended State Observer and Super-Twisting Nonlinear Sliding Mode Control for PMSM Drives," Energies, MDPI, vol. 14(4), pages 1-15, February.
    3. 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.
    4. Qiang Song & Yiting Li & Chao Jia, 2018. "A Novel Direct Torque Control Method Based on Asymmetric Boundary Layer Sliding Mode Control for PMSM," Energies, MDPI, vol. 11(3), pages 1-15, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhenjie Gong & Xin Ba & Chengning Zhang & Youguang Guo, 2022. "Robust Sliding Mode Control of the Permanent Magnet Synchronous Motor with an Improved Power Reaching Law," Energies, MDPI, vol. 15(5), pages 1-13, March.
    2. Roland Kasper & Dmytro Golovakha, 2020. "Combined Optimal Torque Feedforward and Modal Current Feedback Control for Low Inductance PM Motors," Energies, MDPI, vol. 13(23), pages 1-16, November.
    3. Nikola Lopac & Neven Bulic & Niksa Vrkic, 2019. "Sliding Mode Observer-Based Load Angle Estimation for Salient-Pole Wound Rotor Synchronous Generators," Energies, MDPI, vol. 12(9), pages 1-22, April.
    4. Zhiming Liao & Yue Hao & Tao Guo & Bingxin Lv & Qiang Wang, 2022. "Second-Order Sliding Mode Control of Permanent Magnet Synchronous Motor Based on Singular Perturbation," Energies, MDPI, vol. 15(21), pages 1-13, October.
    5. Zhanqing Zhou & Xin Gu & Zhiqiang Wang & Guozheng Zhang & Qiang Geng, 2019. "An Improved Torque Control Strategy of PMSM Drive Considering On-Line MTPA Operation," Energies, MDPI, vol. 12(15), pages 1-17, July.
    6. Liqin Wu & Hao Chen & Tingyue Yu & Chengzhi Sun & Lin Wang & Xuerong Ye & Guofu Zhai, 2023. "Robust Design Optimization of the Cogging Torque for a PMSM Based on Manufacturing Uncertainties Analysis and Approximate Modeling," Energies, MDPI, vol. 16(2), pages 1-24, January.
    7. Hassam Muazzam & Mohamad Khairi Ishak & Athar Hanif & Ali Arshad Uppal & AI Bhatti & Nor Ashidi Mat Isa, 2022. "Virtual Sensor Using a Super Twisting Algorithm Based Uniform Robust Exact Differentiator for Electric Vehicles," Energies, MDPI, vol. 15(5), pages 1-18, February.
    8. Pawel Latosinski & Andrzej Bartoszewicz, 2023. "Sliding Mode Controllers in Energy Systems and Other Applications," Energies, MDPI, vol. 16(3), pages 1-4, January.
    9. Jiachun Lin & Yuteng Zhao & Pan Zhang & Junjie Wang & Hao Su, 2021. "Research on Compound Sliding Mode Control of a Permanent Magnet Synchronous Motor in Electromechanical Actuators," Energies, MDPI, vol. 14(21), pages 1-17, November.
    10. Yuzhe Zhang & Xiaodong Liu & Haitao Li & Zhenbin Zhang, 2023. "A Model Independent Predictive Control of PMSG Wind Turbine Systems with a New Mechanism to Update Variables," Energies, MDPI, vol. 16(9), pages 1-15, April.
    11. Rui Xiong & Suleiman M. Sharkh & Xi Zhang, 2018. "Research Progress on Electric and Intelligent Vehicles," Energies, MDPI, vol. 11(7), pages 1-5, July.
    12. Katarzyna Adamiak & Andrzej Bartoszewicz, 2022. "Novel Power-Rate Reaching Law for Quasi-Sliding Mode Control," Energies, MDPI, vol. 15(15), pages 1-14, July.
    13. Mingyuan Hu & Hyeongki Ahn & Yoonuh Chung & Kwanho You, 2023. "Speed Regulation for PMSM with Super-Twisting Sliding-Mode Controller via Disturbance Observer," Mathematics, MDPI, vol. 11(7), pages 1-15, March.
    14. Kifayat Ullah & Jaroslaw Guzinski & Adeel Feroz Mirza, 2022. "Critical Review on Robust Speed Control Techniques for Permanent Magnet Synchronous Motor (PMSM) Speed Regulation," Energies, MDPI, vol. 15(3), pages 1-13, February.
    15. Feng Jiang & Fan Yang & Songjun Sun & Kai Yang, 2022. "Improved Linear Active Disturbance Rejection Control for IPMSM Drives Considering Load Inertia Mismatch," Energies, MDPI, vol. 15(3), pages 1-22, February.
    16. Abhinandan Routray & Yiza Srikanth Reddy & Sung-ho Hur, 2023. "Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    17. Piotr Leśniewski & Andrzej Bartoszewicz, 2021. "Reaching Law Based Sliding Mode Control of Sampled Time Systems," Energies, MDPI, vol. 14(7), pages 1-19, March.
    18. Habib Benbouhenni & Nicu Bizon, 2021. "Improved Rotor Flux and Torque Control Based on the Third-Order Sliding Mode Scheme Applied to the Asynchronous Generator for the Single-Rotor Wind Turbine," Mathematics, MDPI, vol. 9(18), pages 1-16, September.
    19. Aleš Hace, 2019. "The Advanced Control Approach based on SMC Design for the High-Fidelity Haptic Power Lever of a Small Hybrid Electric Aircraft," Energies, MDPI, vol. 12(15), pages 1-31, August.
    20. Muhammad Usama & Jaehong Kim, 2021. "Low-Speed Transient and Steady-State Performance Analysis of IPMSM for Nonlinear Speed Regulator with Effective Compensation Scheme," Energies, MDPI, vol. 14(20), pages 1-16, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4353-:d:1156962. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.