控制理论(社会学)
转子(电动)
观察员(物理)
粒子群优化
频域
职位(财务)
计算机科学
时域
工程类
算法
人工智能
控制(管理)
物理
计算机视觉
量子力学
机械工程
财务
经济
作者
Xin Liu,Hongyi Qu,Lingwei Meng,Qi Chen,Cong Wang,Qiuliang Wang
出处
期刊:Measurement
[Elsevier]
日期:2023-02-01
卷期号:207: 112305-112305
被引量:2
标识
DOI:10.1016/j.measurement.2022.112305
摘要
Increase in volume in the bearingless permanent magnet slice motor (BPMSM) control system for artificial hearts is the major problem in the existing works. A sensorless control method of the BPMSM for the artificial heart is proposed in the study based on an improved sliding mode observer (SMO) and improved high-frequency injection method. Also, a full-speed rotor position detection method combining the high-frequency injection (FHI) method and sliding mode observer is designed. In this method, an improved high-frequency injection method was designed in the low-speed domain, and an improved sliding-mode observer was designed in the medium–high-speed domain. Besides, the speed domain switching method based on the genetic particle swarm optimization (GAPSO) algorithm was adopted at the critical point of the low-speed domain and the medium and high-speed domain to realize the smooth switching between different estimation methods, and then realize the sensorless control of the rotor in the full speed domain. Secondly, the simulation study by Simulink was used to compare the detection effects of the BPMSM rotor position and speed of the artificial heart pump under different methods are compared in this work. The results show that the speed estimation value and rotor position estimation value of the new method in this paper were closer to the actual value, the position estimation error is 3%, and the speed estimation error was within 1.5%. Further, the experimental study was carried out on the principal prototype of the sensorless control part of the artificial heart to verify the effectiveness of the proposed method. The method proposed in this paper has certain generality in the field of motor sensorless control technology, and has important reference value for researchers in this field.
科研通智能强力驱动
Strongly Powered by AbleSci AI