Sensorless Model Predictive Flux Control of AFFSPM Machine Based on Improved Sliding Mode Observer

被引：0
|
作者：
He J. [1 ]
Zhang W. [1 ]
Zhai L. [2 ]
机构：
[1] School of Electrical Engineering, Nantong University, Jiangsu Province, Nantong
[2] Bosch Innovation Software Development (Wuxi) Co., Ltd., Jiangsu Province, Wuxi
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2023年 / 43卷 / 03期
基金：
中国国家自然科学基金;
关键词：
arcsin saturation function; flux-switching permanent magnet machine; model predictive flux control; sensorless control; sliding mode observer;
D O I：
10.13334/j.0258-8013.pcsee.212463
中图分类号：
学科分类号：
摘要：
A sensorless model predictive flux control method based on improved sliding mode observer (SMO) is proposed in order to improve the accuracy of the position detection for the sensorless control of axial field flux-switching permanent magnet (AFFSPM) machine, and to suppress the torque and flux ripple of the machine. First, based on the traditional sliding mode observer, an arcsin saturation function whose boundary layer is self-adjusting with the sliding mode surface error is designed to weaken the system chattering and improve the system steady-state performance. Then, an extended Kalman filter with amplitude compensation is introduced to replace the low-pass filter, and a software phase-locked loop is used to estimate the rotor position angle and speed, so that the position angle compensation is eliminated, and the accuracy of position observation is improved. Finally, the information of the estimated rotor position angle and speed are fed back to the model predictive flux control module to complete the sensorless control. In this paper, the proposed control method is validated by the theoretical derivation and the research of simulation and experiment to improve the static and dynamic performances of sensorless control for AFFSPM machine effectively. ©2023 Chin.Soc.for Elec.Eng.
引用
收藏
页码：1189 / 1199
页数：10
相关论文
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