{"title":"Position and speed observer for PMSM with unknown stator resistance","authors":"D. Bazylev, A. Pyrkin, A. Bobtsov","doi":"10.23919/ECC.2018.8550536","DOIUrl":null,"url":null,"abstract":"In this paper a new nonlinear parameterization of permanent magnet synchronous motor (PMSM) model is proposed for the case of an uncertain stator resistance. The assumption of known inductance only is applied. After parameterization the regression model of six parameters is obtained from which it becomes possible to reconstruct the resistance and two necessary parameters involved in the position and speed observers design. The dynamic regressor extension and mixing (DREM) estimator is used to provide good performance and fast estimation of a large regression model which is preferable than the standard gradient approach. Simulation results illustrating proposed approach are given.","PeriodicalId":222660,"journal":{"name":"2018 European Control Conference (ECC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 European Control Conference (ECC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ECC.2018.8550536","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
In this paper a new nonlinear parameterization of permanent magnet synchronous motor (PMSM) model is proposed for the case of an uncertain stator resistance. The assumption of known inductance only is applied. After parameterization the regression model of six parameters is obtained from which it becomes possible to reconstruct the resistance and two necessary parameters involved in the position and speed observers design. The dynamic regressor extension and mixing (DREM) estimator is used to provide good performance and fast estimation of a large regression model which is preferable than the standard gradient approach. Simulation results illustrating proposed approach are given.