引用本文:郑洪涛,陈 新,蒋静坪.基于模糊神经网络开关磁阻电动机高性能转矩控制[J].控制理论与应用,2003,20(4):541~546.[点击复制]
ZHENG Hong-tao,CHEN Xin,JIANG Jing-ping.High-grade torque control of switched reluctance motor based on neural-fuzzy network[J].Control Theory and Technology,2003,20(4):541~546.[点击复制]
基于模糊神经网络开关磁阻电动机高性能转矩控制
High-grade torque control of switched reluctance motor based on neural-fuzzy network
摘要点击 1183  全文点击 1753  投稿时间：2001-10-29  修订日期：2002-06-28
查看全文  查看/发表评论  下载PDF阅读器
DOI编号  
  2003,20(4):541-546
中文关键词  开关磁阻电动机  转矩脉动  模糊神经网络
英文关键词  switched reluctance motors  torque ripple  fuzzy-neural network
基金项目  
作者单位E-mail
郑洪涛 浙江大学 电气工程学院, 浙江 杭州 310027
株洲电力机车研究所, 湖南 株洲 41200. 		zhengzju@yahoo.com.cn 
陈 新 浙江大学 电气工程学院, 浙江 杭州 310027 cxzju@so-hu.com 
蒋静坪 浙江大学 电气工程学院, 浙江 杭州 310027 eejiang@dial.zju.edu.cn 
中文摘要
      开关磁阻电动机由于其转矩是各相电流与转子位置角的高度非线性函数, 传统控制方法难以对其达到有效的控制. 应用模糊神经网络对开关磁阻电动机静态转矩特性逆模型进行离线学习, 学习完成之后, 在转矩分配函数的基础上, 实时在线优化出期望转矩所需要的相电流波形, 从而实现开关磁阻电动机的转矩线性、解耦、无脉动控制. 计算机仿真结果证明了这种方法的有效性.
英文摘要
      The primary disadvantage of an SRM was the higher torque ripple which was due to the highly nonlinear and discrete nature of torque production mechanism. Based on the experimental data of static torque characteristic, a fuzzy-neural network(FNN) was applied to the learning of its inverse model off line. Then according to the predefined torque distribution function (TDF), optimal current profile was real-time gained by the FNN on line, which resulted in a linear, decoupled, low ripple control of torque. The effectiveness of the proposed method was demonstrated by computer simulation results.