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A Neural Controller for Induction Motors: Fractional-Order Stability Analysis and Online Learning Algorithm

Author

Listed:
  • Mohammad Hosein Sabzalian

    (Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
    Laboratory of Power Electronics and Medium Voltage Applications (LEMT), The Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941594, Brazil)

  • Khalid A. Alattas

    (Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia)

  • Fayez F. M. El-Sousy

    (Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj 16273, Saudi Arabia)

  • Ardashir Mohammadzadeh

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    School of Engineering and Technology, Duy Tan University, Da Nang 550000, Vietnam)

  • Saleh Mobayen

    (Future Technology Research Center, National Yunlin University of Science and Technology, Douliu 64002, Taiwan)

  • Mai The Vu

    (School of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea)

  • Mauricio Aredes

    (Laboratory of Power Electronics and Medium Voltage Applications (LEMT), The Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941594, Brazil)

Abstract

In this study, an intelligent control scheme is developed for induction motors (IMs). The dynamics of IMs are unknown and are perturbed by the variation of rotor resistance and load changes. The control system has two stages. In the identification stage, the group method of data-handling (GMDH) neural network (NN) was designed for online modeling of the IM. In the control stage, the GMDH-NN was applied to compensate for the impacts of disturbances and uncertainties. The stability is shown by the Lyapunov approach. Simulations demonstrated the good accuracy of the suggested new control approach under disturbances and unknown dynamics.

Suggested Citation

  • Mohammad Hosein Sabzalian & Khalid A. Alattas & Fayez F. M. El-Sousy & Ardashir Mohammadzadeh & Saleh Mobayen & Mai The Vu & Mauricio Aredes, 2022. "A Neural Controller for Induction Motors: Fractional-Order Stability Analysis and Online Learning Algorithm," Mathematics, MDPI, vol. 10(6), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:1003-:d:775793
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    References listed on IDEAS

    as
    1. Rui Li & Liang Yang & Yong Chen & Guanyu Lai, 2022. "Adaptive Sliding Mode Control of Robot Manipulators with System Failures," Mathematics, MDPI, vol. 10(3), pages 1-15, January.
    2. Shaghaghi, Saba & Bonakdari, Hossein & Gholami, Azadeh & Ebtehaj, Isa & Zeinolabedini, Maryam, 2017. "Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 271-286.
    3. Habib Benbouhenni & Nicu Bizon, 2021. "A Synergetic Sliding Mode Controller Applied to Direct Field-Oriented Control of Induction Generator-Based Variable Speed Dual-Rotor Wind Turbines," Energies, MDPI, vol. 14(15), pages 1-17, July.
    4. Habib Benbouhenni & Nicu Bizon, 2021. "Third-Order Sliding Mode Applied to the Direct Field-Oriented Control of the Asynchronous Generator for Variable-Speed Contra-Rotating Wind Turbine Generation Systems," Energies, MDPI, vol. 14(18), pages 1-20, September.
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    Cited by:

    1. Fahimeh Shiravani & Patxi Alkorta & Jose Antonio Cortajarena & Oscar Barambones, 2022. "An Integral Sliding Mode Stator Current Control for Industrial Induction Motor," Mathematics, MDPI, vol. 10(15), pages 1-20, August.
    2. Xin Guo & Hejun Yao & Fangzheng Gao, 2022. "Global Prescribed-Time Stabilization of High-Order Nonlinear Systems with Asymmetric Actuator Dead-Zone," Mathematics, MDPI, vol. 10(12), pages 1-15, June.
    3. Hualin Song & Cheng Hu & Juan Yu, 2022. "Stability and Synchronization of Fractional-Order Complex-Valued Inertial Neural Networks: A Direct Approach," Mathematics, MDPI, vol. 10(24), pages 1-23, December.

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