Unscented particle filter-based state estimation for permanent magnet linear synchronous motor

被引:0
作者
Liu, Xinghua [1 ]
Lv, Yunling [1 ]
Guan, Jianwei [1 ]
机构
[1] Xian Univ Technol, Sch Elect Engn, Xian 710048, Peoples R China
来源
2020 CHINESE AUTOMATION CONGRESS (CAC 2020) | 2020年
关键词
Unscented particle filter; permanent magnet linear synchronous motor; non-position sensor; dynamic state estimation;
D O I
10.1109/CAC51589.2020.9327652
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper investigates the rotor angular velocity state estimation of permanent magnet linear synchronous motor (PMLSM) sensorless system. The velocity feedback is an indispensable link to realize the accurate speed control of PMLSM. Accurate rotor information acquisition is the essential problem of the whole control system, since it is impossible to obtain rotor information by traditional mechanical sensors in the non-position sensor control of PMLSM drive system. To work out this problem, this paper raises a non-position sensor control method based on unscented particle filter (UPF). Firstly, the nonlinear dynamic model is established for PMLSM. In the framework of particle filter (PF), the latest measurement information is to generate the predicted particles. Finally, a numerical example and simulated results show that UPF can accurately estimate the rotor angular velocity.
引用
收藏
页码:5474 / 5479
页数:6
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