{"title":"基于IMC的遗传多变量PID控制器","authors":"Sharareh Kermanshachi, N. Sadati","doi":"10.1109/NAFIPS.2007.383832","DOIUrl":null,"url":null,"abstract":"A new approach for PID tuning, based on GA (Genetic algorithm) and Internal Model Control (IMC) technique, is presented in this paper. PID tuning is based on using Method. The IMC technique reduces the number of parameters that must be tuned for a multivariable system using PID controller. The algorithm uses GA for optimal determination of IMC variables. Simulation results present the good performance of the proposed method.","PeriodicalId":292853,"journal":{"name":"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society","volume":"131 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Genetic Multivariable PID Controller Based on IMC\",\"authors\":\"Sharareh Kermanshachi, N. Sadati\",\"doi\":\"10.1109/NAFIPS.2007.383832\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new approach for PID tuning, based on GA (Genetic algorithm) and Internal Model Control (IMC) technique, is presented in this paper. PID tuning is based on using Method. The IMC technique reduces the number of parameters that must be tuned for a multivariable system using PID controller. The algorithm uses GA for optimal determination of IMC variables. Simulation results present the good performance of the proposed method.\",\"PeriodicalId\":292853,\"journal\":{\"name\":\"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society\",\"volume\":\"131 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAFIPS.2007.383832\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.2007.383832","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new approach for PID tuning, based on GA (Genetic algorithm) and Internal Model Control (IMC) technique, is presented in this paper. PID tuning is based on using Method. The IMC technique reduces the number of parameters that must be tuned for a multivariable system using PID controller. The algorithm uses GA for optimal determination of IMC variables. Simulation results present the good performance of the proposed method.