引用本文:孙晓东,朱熀秋,杨泽斌,张涛.无轴承永磁同步电机最小二乘支持向量机非线性建模[J].控制理论与应用,2012,29(4):524~528.[点击复制]
SUN Xiao-dong,ZHU Huang-qiu,YANG Ze-bin,ZHANG Tao.Nonlinear modeling of bearingless permanent magnet synchronous motors with least squares support vector machiness[J].Control Theory and Technology,2012,29(4):524~528.[点击复制]
无轴承永磁同步电机最小二乘支持向量机非线性建模
Nonlinear modeling of bearingless permanent magnet synchronous motors with least squares support vector machiness
摘要点击 2079  全文点击 1943  投稿时间：2011-01-13  修订日期：2011-06-16
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DOI编号  10.7641/j.issn.1000-8152.2012.4.CCTA110063
  2012,29(4):524-528
中文关键词  无轴承永磁同步电机  非线性模型  最小二乘支持向量机  建模
英文关键词  bearingless permanent magnet synchronous motor  nonlinear model  least squares support vector machines  modeling
基金项目  国家自然科学基金项目资助项目(61104016); 江苏省高校自然科学研究面上基金资助项目(11KJB510002); 国家“863”高技术研究发展计划资助项目(2007AA04Z213); 江苏省2010年研究生科研创新计划资助项目(CX10B 270Z); 江苏高校优势学科建设工程资助项目(苏政办发[2011]6号)．
作者单位E-mail
孙晓东* 江苏大学 汽车工程研究院
江苏大学 电气信息工程学院 		xdsun@ujs.edu.cn 
朱熀秋 江苏大学 电气信息工程学院  
杨泽斌 江苏大学 电气信息工程学院  
张涛 江苏大学 电气信息工程学院  
中文摘要
      无轴承永磁同步电机的磁链特性表现为严重的非线性, 常规的解析法所建立的模型难以准确反映无轴承永磁同步电机的实际特性. 因此, 提出利用最小二乘支持向量机建立无轴承永磁同步电机非线性模型的新方法. 在介绍最小二乘支持向量机回归理论的基础上, 利用有限元法得到的样本建立了无轴承永磁同步电机的最小二乘支持向量机非线性模型, 并与神经网络方法进行了比较. 仿真结果表明, 所建模型具有较好的鲁棒性和预测精度. 最后给出了应用该模型实现无轴承永磁同步电机优化控制的方法.
英文摘要
      The flux linkage characteristic of the bearingless permanent magnet synchronous motor (BPMSM) is highly nonlinear, and the conventional mathematical model established by analysis method can not reflect the real characteristics of the BPMSM. Therefore, a novel modeling method is proposed for the BPMSM to take into account its nonlinearity more accurately by using the least squares support vector machiness (LSSVM). After the regression theory of the LSSVM is introduced, the LSSVM model of the BPMSM is built up by using the sampled data obtained from the experimental prototype with the finite elements method. Moreover, the LSSVM model is compared with the model based on neural network method. Simulation results show that the proposed model has desirable robustness and high accuracy. Finally, the optimal controller based on the modeling for the BPMSM is developed.