|本期目录/Table of Contents|

[1]蒯松岩,张旭隆,王其虎,等.开关磁阻电机神经网络无位置传感器控制[J].电机与控制学报,2011,(08):18-22.
 KUAI Song-yan,ZHANG Xu-long,WANG Qi-hu,et al.Position sensorless control of SRM using neural network[J].,2011,(08):18-22.
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开关磁阻电机神经网络无位置传感器控制(PDF)
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
期数:
2011年08
页码:
18-22
栏目:
出版日期:
2011-08-20

文章信息/Info

Title:
Position sensorless control of SRM using neural network
作者:
蒯松岩; 张旭隆; 王其虎; 张能
中国矿业大学信息与电气工程学院; 江苏省电力传动与自动控制工程技术研究中心;
Author(s):
KUAI Song-yan12ZHANG Xu-long12WANG Qi-hu1ZHANG Neng1
1.School of Information and Electrical Engineering,China University of Mining and Technology,Xuzhou 221008,China;2.Jiangsu Electrical Drive and Control Engineering Research Center,Xuzhou 221116,China
关键词:
神经网络 开关磁阻电动机 无位置传感器 数字信号处理器
Keywords:
neural network switched reluctance motors sensorless digital signal processor
分类号:
TM352
DOI:
-
文献标志码:
A
摘要:
针对现有开关磁阻电机(SRM)的转子位置传感器使得系统成本和复杂度提高、坚固性和可靠性降低的问题,研究了SRM无位置传感器DSP控制实现。建立了开关磁阻电机位置检测神经网络模型,并给出了提出对象的学习算法和训练步骤。采用TMS320F2812 DSP实现神经网络在线训练算法,开发完成了一台15kW三相12/8极无位置传感器SRD样机。实验结果表明,无位置传感器SRD具有较好的动态特性和较高精确度,系统最大位置检测误差≤2°。
Abstract:
As position sensor of switched reluctance motor(SRM) increases system cost and complexity,while reduces robustness and reliability,sensorless control of SRM based on DSP was proposed.A neural network of rotor position estimation for SRM sensorless drive was set up.On-line learning algorithms and training steps were also given.Neural network on-line training algorithm was achieved by TMS320F2812 DSP,and 15kW three-phase 12/8 pole sensorless SRD was set up.Experimental results show that the system...

参考文献/References:

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备注/Memo

备注/Memo:
江苏省自然科学基金(BK2009526);中国矿业大学青年科研基金(2009A025)
更新日期/Last Update: 2012-03-27