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一种改进的RBF神经网络的开关磁阻电机磁链模型 被引量:1

An Improved Flux Linkage Model of Switched Reluctance Motors Based on RBF Neural Networks
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摘要 基于径向基(RBF)神经网络的开关磁阻电机无位置传感器技术在拟合磁链-电流-角度的模型中,初始角度范围内因为磁链的变化率较小,数据点密集,RBF神经网络的聚类中心点难以准确地计算,导致拟合的误差比较大。采用分段的形式对RBF神经网络的开关磁阻电机无位置传感器模型进行改进,即将RBF神经网络模型的小角度范围内的数据分离出来予以单独处理,用线性化的方法对其进行建模通过MATLAB仿真验证了该方法的可行性。 In the process of fitting flux linkage/current/angle in the sensorless techniques of SRM based on RBF neural networks, at a small range of initial angle the rate of change is so small and the sample point is so closed that it is hard to compute the cluster center RBF neural network correctly and the error in fitting is caused. This paper improves the flux linkage/current/angle in the sensorless techniques of SRM by way of piecewise made, it means that the sample data is separated and handled in the small range of the initial angle, then the linearization technique is used to establish the model. It proves the method is available by MATLAB simulation.
机构地区 河海大学
出处 《机械制造与自动化》 2015年第6期172-175,共4页 Machine Building & Automation
关键词 开关磁阻电机 无位置传感器 径向基神经网络 switched reluctance mortor sensorless RBF neural networks
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  • 1杨玉岗.开关磁阻电机最佳起动状态的仿真研究[J].煤炭学报,2004,29(B10):188-191. 被引量:4
  • 2曹家勇,周祖德,陈幼平,詹琼华.开关磁阻电动机特性检测与参数辨识方法研究[J].电工技术学报,2004,19(11):25-30. 被引量:20
  • 3夏长亮,王明超,史婷娜,郭培健.基于神经网络的开关磁阻电机无位置传感器控制[J].中国电机工程学报,2005,25(13):123-128. 被引量:71
  • 4谢德馨,阎秀恪,张奕黄,曾建斌.旋转电机绕组磁链的三维有限元分析[J].中国电机工程学报,2006,26(21):143-148. 被引量:23
  • 5Krishnan R. Switched reluctance motor drives : modeling, simulation, analysis, design and applications [ M ]. Boca Raton, FL: CRC Press, 2001.
  • 6Acarnley P, Hill R J, Hooper C W. Detection of rotor position in stepping and switched reluctance motors by monitoring of current waveforms[ J]. IEEE Transactions on Industrial Electronics, 1988, 32:215-222.
  • 7Gao Hongwei, Farzad Rajaei Salmasi, Mehrdad Ehsani. Inductance model-based sensorless control of the switched reluctance motor drive at low speed [J]. IEEE Transactions on Power Electronics, 2004,19(6) :1 568-1 573.
  • 8Husain 1, Sodhi S, Ehsani M. Sliding mode observer based control for switched reluctance motors [ A ]. Proc. IEEE-IAS Conf. , Rec. [C].Denver, 1994 : 635 -643.
  • 9Baik W-S, Kim M-H, Kim N-H, et al. Position sensorless control system of SRM using neural network [ A J. Proc. IEEE PESC [ C ]. 2004:3 471-3 475.
  • 10Lim H S, Roberson D G, Lobo N S, et al. Novel flux linkage control of switched reluctance motor drives using observer and neural network-based correction methods [ A ]. Proc. IEEE IECON [C]. 2005 : 1 431-1 436.

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