{"title":"直流伺服电机速度控制的神经模糊控制器设计","authors":"Young-Ho Kang, Lark-Kyo Kim","doi":"10.1109/ICEMS.2001.971780","DOIUrl":null,"url":null,"abstract":"The authors have designed a neuro-fuzzy controller to improve some problems that occur when the nonlinear system is controlled by a fuzzy logic controller. Their model obtains fast-time response, maximized learning effect and shortened settling time. To prove the capability of the neuro-fuzzy controller designed in this paper, this neuro-fuzzy model is applied to a DC servomotor. As a result, this controller does not produce overshoot, which occurs in the PID controller, and also does not produce the steady state error of FLC. Also, it shortens the settling time by about 10%. In addition, the authors are aware that their model has only about 60% of the value of current peak of the PID controller.","PeriodicalId":143007,"journal":{"name":"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Design of neuro-fuzzy controller for the speed control of a DC servo motor\",\"authors\":\"Young-Ho Kang, Lark-Kyo Kim\",\"doi\":\"10.1109/ICEMS.2001.971780\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors have designed a neuro-fuzzy controller to improve some problems that occur when the nonlinear system is controlled by a fuzzy logic controller. Their model obtains fast-time response, maximized learning effect and shortened settling time. To prove the capability of the neuro-fuzzy controller designed in this paper, this neuro-fuzzy model is applied to a DC servomotor. As a result, this controller does not produce overshoot, which occurs in the PID controller, and also does not produce the steady state error of FLC. Also, it shortens the settling time by about 10%. In addition, the authors are aware that their model has only about 60% of the value of current peak of the PID controller.\",\"PeriodicalId\":143007,\"journal\":{\"name\":\"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-08-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEMS.2001.971780\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEMS.2001.971780","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design of neuro-fuzzy controller for the speed control of a DC servo motor
The authors have designed a neuro-fuzzy controller to improve some problems that occur when the nonlinear system is controlled by a fuzzy logic controller. Their model obtains fast-time response, maximized learning effect and shortened settling time. To prove the capability of the neuro-fuzzy controller designed in this paper, this neuro-fuzzy model is applied to a DC servomotor. As a result, this controller does not produce overshoot, which occurs in the PID controller, and also does not produce the steady state error of FLC. Also, it shortens the settling time by about 10%. In addition, the authors are aware that their model has only about 60% of the value of current peak of the PID controller.