{"title":"基于神经网络的无刷直流电动机速度控制系统研究","authors":"Shuangqiao Xiong, Gao Junguo, C. Jian, J. Biao","doi":"10.1109/ICICTA.2015.193","DOIUrl":null,"url":null,"abstract":"Considering the shortcomings of low precision, response lag and control instability of conventional PID controller in brushless dc motor speed control system, an intelligent controller is designed, which is combines RBFneural network with PID controller. This paper analyzed operating principle of the brushless dc motor, and deduced transfer function model of the controller. The control programs and control model of the designed controller were also provided. At last, the designed controller was validated through Mat lab software in the simulation. In the process of simulation, three parameters of PID were adjusted obviously by RBF-Neural Network. The results of simulation show that the designed controller is more effectively in improving the control performance and faster in response than conventional PID controller.","PeriodicalId":231694,"journal":{"name":"2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Research on Speed Control System of Brushless DC Motor Based on Neural Network\",\"authors\":\"Shuangqiao Xiong, Gao Junguo, C. Jian, J. Biao\",\"doi\":\"10.1109/ICICTA.2015.193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Considering the shortcomings of low precision, response lag and control instability of conventional PID controller in brushless dc motor speed control system, an intelligent controller is designed, which is combines RBFneural network with PID controller. This paper analyzed operating principle of the brushless dc motor, and deduced transfer function model of the controller. The control programs and control model of the designed controller were also provided. At last, the designed controller was validated through Mat lab software in the simulation. In the process of simulation, three parameters of PID were adjusted obviously by RBF-Neural Network. The results of simulation show that the designed controller is more effectively in improving the control performance and faster in response than conventional PID controller.\",\"PeriodicalId\":231694,\"journal\":{\"name\":\"2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICTA.2015.193\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICTA.2015.193","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Speed Control System of Brushless DC Motor Based on Neural Network
Considering the shortcomings of low precision, response lag and control instability of conventional PID controller in brushless dc motor speed control system, an intelligent controller is designed, which is combines RBFneural network with PID controller. This paper analyzed operating principle of the brushless dc motor, and deduced transfer function model of the controller. The control programs and control model of the designed controller were also provided. At last, the designed controller was validated through Mat lab software in the simulation. In the process of simulation, three parameters of PID were adjusted obviously by RBF-Neural Network. The results of simulation show that the designed controller is more effectively in improving the control performance and faster in response than conventional PID controller.