Position Estimation at Zero Speed for PMSMs Using Artificial Neural Networks

Konrad Urbanski and Dariusz Janiszewski
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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

Energies, 2021, vol. 14, issue 23, 1-17

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.

Keywords: PMSM; permanent magnet motors; sensorless control; estimation; ANN; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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