Sensorless PMSM Drive Inductance Estimation Based on a Data-Driven Approach

• Main Authors: Gwangmin Park, Gyeongil Kim, Bon-Gwan Gu
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Electronics
• Subjects: sensorless control; magnetic saturation; inductance variation; polynomial neural network (PNN); group method of data handling (GMDH); noise, vibration, and harshness (NVH)
• Online Access: https://www.mdpi.com/2079-9292/10/7/791

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.