{"title":"自动化多模模糊控制器设计","authors":"E. M. Hugo, J. du Plessis","doi":"10.1109/ISIC.2001.971539","DOIUrl":null,"url":null,"abstract":"An automated, multi-mode, fuzzy logic controller design method is presented. The method uses the sum of weights method to design consequences of an initial control rule base. The performance of the multi-mode controller is then improved using a model reference adaptive control scheme. The performance of the multi-mode fuzzy logic controller is compared to a self-learning, single-mode fuzzy logic controller using computer simulations and nonlinear plants.","PeriodicalId":367430,"journal":{"name":"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Automated multi-mode fuzzy logic controller design\",\"authors\":\"E. M. Hugo, J. du Plessis\",\"doi\":\"10.1109/ISIC.2001.971539\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An automated, multi-mode, fuzzy logic controller design method is presented. The method uses the sum of weights method to design consequences of an initial control rule base. The performance of the multi-mode controller is then improved using a model reference adaptive control scheme. The performance of the multi-mode fuzzy logic controller is compared to a self-learning, single-mode fuzzy logic controller using computer simulations and nonlinear plants.\",\"PeriodicalId\":367430,\"journal\":{\"name\":\"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIC.2001.971539\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIC.2001.971539","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An automated, multi-mode, fuzzy logic controller design method is presented. The method uses the sum of weights method to design consequences of an initial control rule base. The performance of the multi-mode controller is then improved using a model reference adaptive control scheme. The performance of the multi-mode fuzzy logic controller is compared to a self-learning, single-mode fuzzy logic controller using computer simulations and nonlinear plants.