Abstract

In the permanent magnet synchronous motor (PMSM) sensorless drive method, motor inductance is a decisive parameter for rotor position estimation. Due to core magnetic saturation, the motor current easily invokes inductance variation and degrades rotor position estimation accuracy. For a constant load torque, saturated inductance and inductance error in the sensorless drive method are constant. Inductance error results in constant rotor position estimation error and minor degradations, such as less optimal torque current, but no speed estimation error. For a periodic load torque, the inductance parameter error periodically fluctuates and, as a result, the position estimation error and speed error also periodically fluctuate. Periodic speed error makes speed regulation and load torque compensation especially difficult. This paper presents an inductance parameter estimator based on polynomial neural network (PNN) machine learning for PMSM sensorless drive with a period load torque compensator. By applying an inductance estimator, we also proposed a magnetic saturation compensation method to minimize periodic speed fluctuation. Simulation and experiments were conducted to validate the proposed method by confirming improved position and speed estimation accuracy and reduced system vibration against periodic load torque.

Highlights

  • Permanent magnet synchronous motors (PMSMs) are largely applied to home appliances and automotive motors owing to their high efficiency and lightweight features

  • To minimize the uncertainty based on magnetic saturation and improve real-time control performance, we proposed a polynomial neural network (PNN)-based compensation method using a group method of data handling (GMDH) algorithm to estimate the q-axis inductance and perform compensation control for magnetic saturation

  • This paper analyzed the position and speed estimation error of a PMSM sensorless This paper analyzed the position and speed estimation error of a PMSM sensorless drive caused by magnetic saturation

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Summary

Introduction

Permanent magnet synchronous motors (PMSMs) are largely applied to home appliances and automotive motors owing to their high efficiency and lightweight features. For a high performance PMSM drive, a rotor field-oriented control method is widely used. In this method, accurate position information of the rotor must be identified in real time. The synchronous reference frame model-based sensorless control method is largely classified into current model-based and extended electromotive force (EMF)-based methods [2,3]. The latter method is commonly used because of its fast-tracking capability using the arc-tangent calculation

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