Phi Hoang Nha, Phạm Hùng Phi, Dao Quang Thuy, Lê Xuân Hải, Pham Xuan Dat, N. N. Linh
{"title":"基于人工神经网络磁链估计的开关磁阻电机反步控制","authors":"Phi Hoang Nha, Phạm Hùng Phi, Dao Quang Thuy, Lê Xuân Hải, Pham Xuan Dat, N. N. Linh","doi":"10.25073/2588-1086/vnucsce.296","DOIUrl":null,"url":null,"abstract":"The paper presents a new approach to design a nonlinear controller for switched reluctance motors (SRMs) based on backstepping technique and artificial neuron network (ANN) in flux estimator. Backstepping controller with an ANN flux estimator will be applied for controlling SRMs which have a nonlinear drive model. The ANN flux estimator was trained off-line using backpropagation algorithm. The stability of the closed control loop was analyzed and proved accroding to the Lyapunov stability standard. The numerical simulation results confirmed the accuracy of the estimator and the quality of the backstepping control system.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Backstepping Control of Switched Reluctance Motor with Artificial Neural Network based Flux Estimator\",\"authors\":\"Phi Hoang Nha, Phạm Hùng Phi, Dao Quang Thuy, Lê Xuân Hải, Pham Xuan Dat, N. N. Linh\",\"doi\":\"10.25073/2588-1086/vnucsce.296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents a new approach to design a nonlinear controller for switched reluctance motors (SRMs) based on backstepping technique and artificial neuron network (ANN) in flux estimator. Backstepping controller with an ANN flux estimator will be applied for controlling SRMs which have a nonlinear drive model. The ANN flux estimator was trained off-line using backpropagation algorithm. The stability of the closed control loop was analyzed and proved accroding to the Lyapunov stability standard. The numerical simulation results confirmed the accuracy of the estimator and the quality of the backstepping control system.\",\"PeriodicalId\":416488,\"journal\":{\"name\":\"VNU Journal of Science: Computer Science and Communication Engineering\",\"volume\":\"77 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"VNU Journal of Science: Computer Science and Communication Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.25073/2588-1086/vnucsce.296\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"VNU Journal of Science: Computer Science and Communication Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25073/2588-1086/vnucsce.296","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Backstepping Control of Switched Reluctance Motor with Artificial Neural Network based Flux Estimator
The paper presents a new approach to design a nonlinear controller for switched reluctance motors (SRMs) based on backstepping technique and artificial neuron network (ANN) in flux estimator. Backstepping controller with an ANN flux estimator will be applied for controlling SRMs which have a nonlinear drive model. The ANN flux estimator was trained off-line using backpropagation algorithm. The stability of the closed control loop was analyzed and proved accroding to the Lyapunov stability standard. The numerical simulation results confirmed the accuracy of the estimator and the quality of the backstepping control system.