{"title":"Sensorless speed control of Brushless DC Motor using fuzzy controller","authors":"J. S. Rao, G. R. Kumar, O. Sekhar","doi":"10.1109/ICEEOT.2016.7755483","DOIUrl":null,"url":null,"abstract":"This paper proposes a fuzzy controlled integrated speed - Sensorless approach for the speed control of Brushless DC Motor (BLDCM). This speed sensorless approach employs a load observer to estimate the disturbed load torque, and thus develops a speed sensorless algorithm. For the load observer, the inputs are mechanical rotor inertia constant and the friction coefficient, which are estimated using the recursive least-square rule. Thus this approach is insensitive to motor parameter variations and integrated drift problem. The proposed algorithm is simple when compared to extended Kalman filter in estimating the speed. A comparison is made among fuzzy controller, modified model reference adaptive control and PI controller. It is found that the fuzzy controller has superior performance over other two controllers. The proposed scheme is simulated using MATLAB/Simulink.","PeriodicalId":383674,"journal":{"name":"2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEOT.2016.7755483","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This paper proposes a fuzzy controlled integrated speed - Sensorless approach for the speed control of Brushless DC Motor (BLDCM). This speed sensorless approach employs a load observer to estimate the disturbed load torque, and thus develops a speed sensorless algorithm. For the load observer, the inputs are mechanical rotor inertia constant and the friction coefficient, which are estimated using the recursive least-square rule. Thus this approach is insensitive to motor parameter variations and integrated drift problem. The proposed algorithm is simple when compared to extended Kalman filter in estimating the speed. A comparison is made among fuzzy controller, modified model reference adaptive control and PI controller. It is found that the fuzzy controller has superior performance over other two controllers. The proposed scheme is simulated using MATLAB/Simulink.