Improvement of Low-speed Sensorless Control with Multi-Layer Neural Network

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  • Maekawa Sari
    Department of Systems Design Engineering, Faculty of Science and Technology, Seikei University
  • Tanaka Ami
    Department of Systems Design Engineering, Faculty of Science and Technology, Seikei University

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  • 多層ニューラルネットワークを用いたPMSMの低速域センサレス制御の高性能化
  • タソウ ニューラルネットワーク オ モチイタ PMSM ノ テイソクイキ センサレス セイギョ ノ コウセイノウカ

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<p>In recent years, there has been an increasing demand for position sensorless control in PMSM drives, and various methods have been studied. Switching noise is a problem in the low-speed sensorless control method that uses the current slope during PWM. Furthermore, another problem is that the inductance does not appear in a sinusoidal distribution owing to magnetic saturation.</p><p>In this paper, we improve the sensorless control method that estimates the position from the current slope during PWM, which is greatly affected by switching. Additionally, we build a multi-layer neural network (NN) that directly estimates the position signals by learning a large amount of current data, and verify the driving results in the low-speed range when the learned NN is incorporated into real-time control.</p>

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