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Position Estimation at Zero Speed for PMSMs Using Artificial Neural Networks

Author

Listed:
  • Konrad Urbanski

    (Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznan, Poland)

  • Dariusz Janiszewski

    (Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznan, Poland)

Abstract

This paper presents a method for shaft position estimation of a synchronous motor with permanent magnets. Zero speed and very low speed range are considered. The method uses the analysis of high-frequency currents induced by the introduction of additional voltage in the control path in the stationary coordinate system associated with the stator. An artificial neural network estimates the sine and cosine values necessary in the Park’s transformation units. This method can achieve satisfactory accuracy in the case of low asymmetry of inductance in the direct and quadrature axes of the coordinate system associated with the rotor. The TensorFlow/Keras package was used for artificial network calculations and the scikit-learn package for preprocessing. Aggregating the outputs of several artificial neural networks provides an opportunity to reduce the resultant estimation error. The use of as few as four networks has enabled the error to be reduced by approximately 20% compared to a single example network.

Suggested Citation

  • Konrad Urbanski & Dariusz Janiszewski, 2021. "Position Estimation at Zero Speed for PMSMs Using Artificial Neural Networks," Energies, MDPI, vol. 14(23), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8134-:d:694976
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    References listed on IDEAS

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    1. Jaime Pando-Acedo & Enrique Romero-Cadaval & Maria Isabel Milanes-Montero & Fermin Barrero-Gonzalez, 2020. "Improvements on a Sensorless Scheme for a Surface-Mounted Permanent Magnet Synchronous Motor Using Very Low Voltage Injection," Energies, MDPI, vol. 13(11), pages 1-17, May.
    2. Malinowski, M. & Kazmierkowski, M.P. & Trzynadlowski, A., 2003. "Review and comparative study of control techniques for three-phase PWM rectifiers," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 63(3), pages 349-361.
    3. Hanying Gao & Wen Zhang & Yu Wang & Zhuo Chen, 2019. "Fault-Tolerant Control Strategy for 12-Phase Permanent Magnet Synchronous Motor," Energies, MDPI, vol. 12(18), pages 1-17, September.
    4. Andrzej Łebkowski, 2018. "Design, Analysis of the Location and Materials of Neodymium Magnets on the Torque and Power of In-Wheel External Rotor PMSM for Electric Vehicles," Energies, MDPI, vol. 11(9), pages 1-23, August.
    5. Krzysztof Szabat & Karol Wróbel & Krzysztof Dróżdż & Dariusz Janiszewski & Tomasz Pajchrowski & Adrian Wójcik, 2020. "A Fuzzy Unscented Kalman Filter in the Adaptive Control System of a Drive System with a Flexible Joint," Energies, MDPI, vol. 13(8), pages 1-18, April.
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    Cited by:

    1. Alessandro Benevieri & Lorenzo Carbone & Simone Cosso & Krishneel Kumar & Mario Marchesoni & Massimiliano Passalacqua & Luis Vaccaro, 2022. "Surface Permanent Magnet Synchronous Motors’ Passive Sensorless Control: A Review," Energies, MDPI, vol. 15(20), pages 1-26, October.

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