{"title":"Sensorless and On-line Parameter Estimation with Model Predictive Control of IPMSMs","authors":"M. X. Bui, Le Khac Thuy, D. Xiao, M. F. Rahman","doi":"10.1109/IECON48115.2021.9589316","DOIUrl":null,"url":null,"abstract":"This paper proposes a sensorless and inductance estimation methods with model predictive control for the interior permanent magnet synchronous motor (IPMSM). The model predictive direct torque and flux control with constant PWM cycle is applied to the control system. The rotor speed and position are estimated based on the current slopes at one active and one zero volage vector during every PWM period where the duty cycle of the active voltage vector is controlled. In addition, the prediction of machine torque and flux is enhanced by the online identification of machine inductances. Extensive numerical simulation has been implemented to validate the robustness and the effectiveness of the proposed sensorless and inductance estimation methods.","PeriodicalId":443337,"journal":{"name":"IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IECON48115.2021.9589316","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper proposes a sensorless and inductance estimation methods with model predictive control for the interior permanent magnet synchronous motor (IPMSM). The model predictive direct torque and flux control with constant PWM cycle is applied to the control system. The rotor speed and position are estimated based on the current slopes at one active and one zero volage vector during every PWM period where the duty cycle of the active voltage vector is controlled. In addition, the prediction of machine torque and flux is enhanced by the online identification of machine inductances. Extensive numerical simulation has been implemented to validate the robustness and the effectiveness of the proposed sensorless and inductance estimation methods.