C. Griva, F. Profumo, C. Ilas, R. Magureanu, P. Vranka
{"title":"基于各种模型参考方案的无速度传感器感应电机磁场定向驱动的统一方法","authors":"C. Griva, F. Profumo, C. Ilas, R. Magureanu, P. Vranka","doi":"10.1109/IAS.1996.559281","DOIUrl":null,"url":null,"abstract":"In this paper a unified approach to different schemes based on model reference adaptive systems (MRAS) for speed sensorless field oriented controlled (FOC) induction motor drives is presented. In the last few years several solutions belonging to this category have been proposed. They have different configurations and distinct adaptation mechanisms, properly chosen in each case. A new, general adaptation mechanism is presented in the paper. It is derived according to Popov hyperstability theory and is valid for any adaptive system belonging to this category, no matter what its configuration is. A general demonstration for the stability of these adaptive systems is given, using the Lyapunov stability theorem. This unitary approach allows an easier comparison and classification of different particular solutions. The paper focuses on two of the most used configurations. In the first solution the reference model is the motor and the adaptive one is a linear state observer, which, in particular, is an extended Luenberger observer (ELO). In the second solution, both models are rotor flux (or other quantities) estimators and this scheme is usually known as a model reference adaptive system (MRAS). The performance of these two schemes is analyzed starting from their configuration and then compared by simulations and experimental results.","PeriodicalId":177291,"journal":{"name":"IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"A unitary approach to speed sensorless induction motor field oriented drives based on various model reference schemes\",\"authors\":\"C. Griva, F. Profumo, C. Ilas, R. Magureanu, P. Vranka\",\"doi\":\"10.1109/IAS.1996.559281\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper a unified approach to different schemes based on model reference adaptive systems (MRAS) for speed sensorless field oriented controlled (FOC) induction motor drives is presented. In the last few years several solutions belonging to this category have been proposed. They have different configurations and distinct adaptation mechanisms, properly chosen in each case. A new, general adaptation mechanism is presented in the paper. It is derived according to Popov hyperstability theory and is valid for any adaptive system belonging to this category, no matter what its configuration is. A general demonstration for the stability of these adaptive systems is given, using the Lyapunov stability theorem. This unitary approach allows an easier comparison and classification of different particular solutions. The paper focuses on two of the most used configurations. In the first solution the reference model is the motor and the adaptive one is a linear state observer, which, in particular, is an extended Luenberger observer (ELO). In the second solution, both models are rotor flux (or other quantities) estimators and this scheme is usually known as a model reference adaptive system (MRAS). The performance of these two schemes is analyzed starting from their configuration and then compared by simulations and experimental results.\",\"PeriodicalId\":177291,\"journal\":{\"name\":\"IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAS.1996.559281\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAS.1996.559281","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A unitary approach to speed sensorless induction motor field oriented drives based on various model reference schemes
In this paper a unified approach to different schemes based on model reference adaptive systems (MRAS) for speed sensorless field oriented controlled (FOC) induction motor drives is presented. In the last few years several solutions belonging to this category have been proposed. They have different configurations and distinct adaptation mechanisms, properly chosen in each case. A new, general adaptation mechanism is presented in the paper. It is derived according to Popov hyperstability theory and is valid for any adaptive system belonging to this category, no matter what its configuration is. A general demonstration for the stability of these adaptive systems is given, using the Lyapunov stability theorem. This unitary approach allows an easier comparison and classification of different particular solutions. The paper focuses on two of the most used configurations. In the first solution the reference model is the motor and the adaptive one is a linear state observer, which, in particular, is an extended Luenberger observer (ELO). In the second solution, both models are rotor flux (or other quantities) estimators and this scheme is usually known as a model reference adaptive system (MRAS). The performance of these two schemes is analyzed starting from their configuration and then compared by simulations and experimental results.