{"title":"一种简单的递归最小二乘法求解FOC永磁同步电机驱动离线参数辨识","authors":"Ramazan Calik, Reha Ozgur Simsek, O. C. Kivanc","doi":"10.1109/GEC55014.2022.9986582","DOIUrl":null,"url":null,"abstract":"A simple and efficient parameter identification algorithm for a permanent magnet synchronous motor (PMSM) drive system is proposed using a recursive least square (RLS) algorithm without any additional sensor. Servo motor control system requires parameter identification due to aging, parameter changes, and mechanical components for driving the reliability and performance of PMSMs. Regarding industrial applications, model-based prediction algorithms are generally expected to be developed. The most important reason is that complex mathematical operations and advanced digital filters are unsuitable for low-cost processors. In this study, all motor parameters (stator resistance, stator inductance, flux linkage, viscous friction, and inertia) are identified using the RLS method as offline. The proposed parameter estimation method for PMSM has been implemented by using the RLS algorithm. Experimental results demonstrate the feasibility and effectiveness of the proposed method.","PeriodicalId":280565,"journal":{"name":"2022 Global Energy Conference (GEC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Simple Recursive Least Squares Approach for Off-line Parameter Identification of FOC Permanent Magnet Synchronous Machine Drive\",\"authors\":\"Ramazan Calik, Reha Ozgur Simsek, O. C. Kivanc\",\"doi\":\"10.1109/GEC55014.2022.9986582\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A simple and efficient parameter identification algorithm for a permanent magnet synchronous motor (PMSM) drive system is proposed using a recursive least square (RLS) algorithm without any additional sensor. Servo motor control system requires parameter identification due to aging, parameter changes, and mechanical components for driving the reliability and performance of PMSMs. Regarding industrial applications, model-based prediction algorithms are generally expected to be developed. The most important reason is that complex mathematical operations and advanced digital filters are unsuitable for low-cost processors. In this study, all motor parameters (stator resistance, stator inductance, flux linkage, viscous friction, and inertia) are identified using the RLS method as offline. The proposed parameter estimation method for PMSM has been implemented by using the RLS algorithm. Experimental results demonstrate the feasibility and effectiveness of the proposed method.\",\"PeriodicalId\":280565,\"journal\":{\"name\":\"2022 Global Energy Conference (GEC)\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Global Energy Conference (GEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GEC55014.2022.9986582\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Global Energy Conference (GEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GEC55014.2022.9986582","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Simple Recursive Least Squares Approach for Off-line Parameter Identification of FOC Permanent Magnet Synchronous Machine Drive
A simple and efficient parameter identification algorithm for a permanent magnet synchronous motor (PMSM) drive system is proposed using a recursive least square (RLS) algorithm without any additional sensor. Servo motor control system requires parameter identification due to aging, parameter changes, and mechanical components for driving the reliability and performance of PMSMs. Regarding industrial applications, model-based prediction algorithms are generally expected to be developed. The most important reason is that complex mathematical operations and advanced digital filters are unsuitable for low-cost processors. In this study, all motor parameters (stator resistance, stator inductance, flux linkage, viscous friction, and inertia) are identified using the RLS method as offline. The proposed parameter estimation method for PMSM has been implemented by using the RLS algorithm. Experimental results demonstrate the feasibility and effectiveness of the proposed method.