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Artificial Intelligence for the Control of Speed of the Bearing Motor with Winding Split Using DSP

Author

Listed:
  • José Raimundo Dantas Neto

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, Brazil
    These authors contributed equally to this work.)

  • José Soares Batista Lopes

    (Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN), Natal 59015-300, Brazil
    These authors contributed equally to this work.)

  • Diego Antonio De Moura Fonsêca

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, Brazil
    These authors contributed equally to this work.)

  • Antonio Ronaldo Gomes Garcia

    (Department of Natural Sciences, Mathematics, and Statistics, Federal Rural University of Semi-Arid (DCME-UFERSA), Mossoró 59625-900, Brazil
    These authors contributed equally to this work.)

  • Jossana Maria de Souza Ferreira

    (School of Science & Technology, Federal University of Rio Grande do Norte (ECT-UFRN), Natal 59072-970, Brazil
    These authors contributed equally to this work.)

  • Elmer Rolando Llanos Villarreal

    (Department of Natural Sciences, Mathematics, and Statistics, Federal Rural University of Semi-Arid (DCME-UFERSA), Mossoró 59625-900, Brazil
    These authors contributed equally to this work.)

  • Andrés Ortiz Salazar

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, Brazil
    These authors contributed equally to this work.)

Abstract

This article describes the study and digital implementation of a system onboard a TMS 3208F28335 ® DSP for vector control of the bearing motor speed with four poles split winding with 250 W of power. Smart techniques: ANFIS and Neural Networks were investigated and computationally implemented to evaluate the bearing motor performance under the following conditions: operating as an estimator of uncertain parameters and as a speed controller. Therefore, the MATLAB program and its toolbox were used for the simulations and the parameter adjustments involving the structure ANFIS (Adaptive-Network-Based Fuzzy Inference System) and simulations with the Neural Network. The simulated results showed a good performance for the two techniques applied differently: the estimator and a speed controller using both a model of the induction motor operating as a bearing motor. The experimental part for velocity vector control uses three control loops: current, radial position, and speed, where the configurations of the peripherals, that is, the interfaces or drivers for driving the bearing motor.

Suggested Citation

  • José Raimundo Dantas Neto & José Soares Batista Lopes & Diego Antonio De Moura Fonsêca & Antonio Ronaldo Gomes Garcia & Jossana Maria de Souza Ferreira & Elmer Rolando Llanos Villarreal & Andrés Ortiz, 2024. "Artificial Intelligence for the Control of Speed of the Bearing Motor with Winding Split Using DSP," Energies, MDPI, vol. 17(5), pages 1-28, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1029-:d:1343824
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    References listed on IDEAS

    as
    1. Rodrigo de Andrade Teixeira & Werbet Luiz Almeida da Silva & Adson Emanuel Santos Amaral & Walter Martins Rodrigues & Andrés Ortiz Salazar & Elmer Rolando Llanos Villarreal, 2023. "Application of Active Disturbance Rejection in a Bearingless Machine with Split-Winding," Energies, MDPI, vol. 16(7), pages 1-16, March.
    2. Zhixin Fu & Zihao Zhou & Junpeng Zhu & Yue Yuan, 2023. "Condition Monitoring Method for the Gearboxes of Offshore Wind Turbines Based on Oil Temperature Prediction," Energies, MDPI, vol. 16(17), pages 1-17, August.
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