Accurate Torque Modeling With PSO-Based Recursive Robust LSSVR for a Segmented-Rotor Switched Reluctance Motor

查看全文 作　　者：Jiangling [1]Wu;Xiaodong [1]Sun;Jianguo [2]Zhu

机构地区：[1]Automotive Engineering Research Institute,Jiangsu University,Zhenjiang 212013,China;[2]School of Electrical and Information Engineering,University of Sydney,NSW 2006,Australia

出　　处：《CES Transactions on Electrical Machines and Systems》索引2020年第4卷第2期,共9页

基　　金：This work was supported by the National Natural Science Foundation of China under Project 51875261;the Natural Science Foundation of Jiangsu Province of China under Projects BK20180046 and BK20170071;the“Qinglan project”of Jiangsu Province,and the Key Project of Natural Science Foundation of Jiangsu Higher Education Institutions under Project 17KJA460005. 摘　　要：In order to improve the reliability in torque calculation of SRM,an accurate nonlinear torque model regresses by recursive robust least squares support vector regression(RR-LSSVR)is proposed in this paper.The model is in terms of a segmented-rotor switched reluctance motor(SSRM).The characteristics of the SSRM is introduced to show its nonlinear characteristics both in magnetic and torque.Then,its mathematic model is established,and an accurate inductance measurement method and a torque calculation method are presented.After this,the principle of the RR-LSSVR and why it can adjust weights according to errors are described.The model used the RR-LSSVR algorithm shows an outstanding capability in accuracy and quickness compared with other algorithms.Finally,to further validate the accuracy of the proposed model in practical application,simulation and experiment are designed based on a 16/10 SSRM. 关键词：Switched reluctance motor intelligent algorithm nonlinear modeling torque calculation LSSVR

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Accurate Torque Modeling With PSO-Based Recursive Robust LSSVR for a Segmented-Rotor Switched Reluctance Motor

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