{"title":"无刷直流电动机建模与验证","authors":"M. S. Hussin, M. N. Azuwir, Y. N. Zaiazmin","doi":"10.1109/ICMSAO.2011.5775620","DOIUrl":null,"url":null,"abstract":"A black box modeling for a Brushless Direct Current motor is developed and simulated based on real-time data. Taking a discrete time form for the system model, an ARX model structure was selected in this work. The real-time data acquired using a Contact Multimeter Probes via NI data acquisition system from a Brushless Direct Current motor. A Pseudo-Random Binary Sequence has been used as the input signal to determine the open-loop model of brushless motor at determined speeds. Input signal and measured data were interfaced to the plant via Matlab programming. Matlab toolbox was used to obtain the estimates model. The model validation was produced by model output plot which is comparing the input output and gives the percent best fit.","PeriodicalId":6383,"journal":{"name":"2011 Fourth International Conference on Modeling, Simulation and Applied Optimization","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Modeling and validation of brushless DC motor\",\"authors\":\"M. S. Hussin, M. N. Azuwir, Y. N. Zaiazmin\",\"doi\":\"10.1109/ICMSAO.2011.5775620\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A black box modeling for a Brushless Direct Current motor is developed and simulated based on real-time data. Taking a discrete time form for the system model, an ARX model structure was selected in this work. The real-time data acquired using a Contact Multimeter Probes via NI data acquisition system from a Brushless Direct Current motor. A Pseudo-Random Binary Sequence has been used as the input signal to determine the open-loop model of brushless motor at determined speeds. Input signal and measured data were interfaced to the plant via Matlab programming. Matlab toolbox was used to obtain the estimates model. The model validation was produced by model output plot which is comparing the input output and gives the percent best fit.\",\"PeriodicalId\":6383,\"journal\":{\"name\":\"2011 Fourth International Conference on Modeling, Simulation and Applied Optimization\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-04-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Fourth International Conference on Modeling, Simulation and Applied Optimization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMSAO.2011.5775620\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Fourth International Conference on Modeling, Simulation and Applied Optimization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMSAO.2011.5775620","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A black box modeling for a Brushless Direct Current motor is developed and simulated based on real-time data. Taking a discrete time form for the system model, an ARX model structure was selected in this work. The real-time data acquired using a Contact Multimeter Probes via NI data acquisition system from a Brushless Direct Current motor. A Pseudo-Random Binary Sequence has been used as the input signal to determine the open-loop model of brushless motor at determined speeds. Input signal and measured data were interfaced to the plant via Matlab programming. Matlab toolbox was used to obtain the estimates model. The model validation was produced by model output plot which is comparing the input output and gives the percent best fit.