Interior Permanent Magnet Synchronous Motor Drive System with Machine Learning-Based Maximum Torque per Ampere and Flux-Weakening Control

Energies

Wei-Ting Lin¹  Jyun-Ru Lin¹  Faa-Jeng Lin¹  Yi-Hung Liao¹ 

[1] Department of Electrical Engineering, National Central University, 320 Taoyuan, Taiwan;

关键词: interior permanent magnet synchronous motor (IPMSM);    maximum torque per ampere (MTPA) control;    flux-weakening (FW) control;    Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF);    adaptive complementary sliding mode (ACSM) control;   

DOI  :  10.3390/en14020346

来源: DOAJ

【 摘 要 】

An interior permanent magnet synchronous motor (IPMSM) drive system with machine learning-based maximum torque per ampere (MTPA) as well as flux-weakening (FW) control was developed and is presented in this study. Since the control performance of IPMSM varies significantly due to the temperature variation and magnetic saturation, a machine learning-based MTPA control using a Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) was developed. First, the d-axis current command, which can achieve the MTPA control of the IPMSM, is derived. Then, the difference value of the dq-axis inductance of the IPMSM is obtained by the PPFNN-AMF and substituted into the d-axis current command of the MTPA to alleviate the saturation effect in the constant torque region. Moreover, a voltage control loop, which can limit the inverter output voltage to the maximum output voltage of the inverter at high-speed, is designed for the FW control in the constant power region. In addition, an adaptive complementary sliding mode (ACSM) speed controller is developed to improve the transient response of the speed control. Finally, some experimental results are given to demonstrate the validity of the proposed high-performance control strategies.

【 授权许可】

Unknown