Modeling of Bearingless Switched Reluctance Motor Based on Artificial Neural Network

摘要：

Accurately calculating and modeling of the flux linkage characteristics is a critical step in design and analysis of optimal control strategies for Bearingless Switched Reluctance Motor (BSRM). But due to the highly nonlinear characteristics of BSRM, it is very difficult to derive a comprehensive mathematical model to satisfy the overall characteristics of the machine. To overcome this problem, in this paper, an edge finite element method (EFE) based 3-D FEM and a new enhanced incremental energy method were utilized for calculating the flux linkage characteristics; using the calculated flux data, an adaptive neural fuzzy inference system (ANFIS) based flux model was developed. Simulation results are presented and compared with the analytical nonlinear modeling method, which verified that the proposed model can modeling the BSRM more accurately and adaptively.

关键词： Bearingless Switched Reluctance Motors FEM Modeling FluxLinkage ANFIS

作者: Cai Jun Deng Zhiquan Qi Ruiyun Liu Zeyuan

作者单位: Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

会议类型: 国际会议

会议名称: The Twelfth International Symposium on Magnetic Bearings(第十二届国际磁悬浮轴承学术会议)

会议地点: 武汉

会议语种:英文

页码: 33-40

在线出版日期: 2010-08-22（万方平台首次上网日期，不代表论文的发表时间）