{"title":"采用遗传算法优化了四相SRM控制器的性能","authors":"S. Poorani","doi":"10.1109/ICIEA.2010.5517056","DOIUrl":null,"url":null,"abstract":"This paper presents the idea of using the Switched Reluctance Motor (SRM) as an alternative to previously used drives, in wide good and other industrial applications. In order to show the advantage of the SRM, the speed control of a switched reluctance motor (SRM) is designed by blending two artificial intelligence techniques, genetic algorithms and fuzzy PI control. Here the Genetic Algorithm (GA) is used to optimize the rules of fuzzy inference system. The importance of the fuzzy PI controller is highlighted by comparing the performance of various control approaches, including PI control and fuzzy control for speed control of SRM motor drive in terms of rise time, settling time, overshoot and it is optimized using GA.","PeriodicalId":234296,"journal":{"name":"2010 5th IEEE Conference on Industrial Electronics and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Performance of 4 phase SRM for various controllers and optimized using genetic algorithm\",\"authors\":\"S. Poorani\",\"doi\":\"10.1109/ICIEA.2010.5517056\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the idea of using the Switched Reluctance Motor (SRM) as an alternative to previously used drives, in wide good and other industrial applications. In order to show the advantage of the SRM, the speed control of a switched reluctance motor (SRM) is designed by blending two artificial intelligence techniques, genetic algorithms and fuzzy PI control. Here the Genetic Algorithm (GA) is used to optimize the rules of fuzzy inference system. The importance of the fuzzy PI controller is highlighted by comparing the performance of various control approaches, including PI control and fuzzy control for speed control of SRM motor drive in terms of rise time, settling time, overshoot and it is optimized using GA.\",\"PeriodicalId\":234296,\"journal\":{\"name\":\"2010 5th IEEE Conference on Industrial Electronics and Applications\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 5th IEEE Conference on Industrial Electronics and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIEA.2010.5517056\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 5th IEEE Conference on Industrial Electronics and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2010.5517056","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance of 4 phase SRM for various controllers and optimized using genetic algorithm
This paper presents the idea of using the Switched Reluctance Motor (SRM) as an alternative to previously used drives, in wide good and other industrial applications. In order to show the advantage of the SRM, the speed control of a switched reluctance motor (SRM) is designed by blending two artificial intelligence techniques, genetic algorithms and fuzzy PI control. Here the Genetic Algorithm (GA) is used to optimize the rules of fuzzy inference system. The importance of the fuzzy PI controller is highlighted by comparing the performance of various control approaches, including PI control and fuzzy control for speed control of SRM motor drive in terms of rise time, settling time, overshoot and it is optimized using GA.
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