{"title":"稀疏控制器的直接数据驱动设计","authors":"S. Formentin, A. Karimi","doi":"10.1109/ACC.2013.6580307","DOIUrl":null,"url":null,"abstract":"This paper deals with direct data-driven design of model-reference controllers whose number of parameters is constrained. Input-output (I/O) sparse controllers are introduced and proposed as an alternative to low-order controller tuning. The optimal I/O sparse controller is shown to be never worse than the optimal low-order controller with the same number of parameters and a suited design procedure based on convex optimization is derived. The theoretical concepts are illustrated by means of a benchmark simulation example.","PeriodicalId":145065,"journal":{"name":"2013 American Control Conference","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Direct data-driven design of sparse controllers\",\"authors\":\"S. Formentin, A. Karimi\",\"doi\":\"10.1109/ACC.2013.6580307\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper deals with direct data-driven design of model-reference controllers whose number of parameters is constrained. Input-output (I/O) sparse controllers are introduced and proposed as an alternative to low-order controller tuning. The optimal I/O sparse controller is shown to be never worse than the optimal low-order controller with the same number of parameters and a suited design procedure based on convex optimization is derived. The theoretical concepts are illustrated by means of a benchmark simulation example.\",\"PeriodicalId\":145065,\"journal\":{\"name\":\"2013 American Control Conference\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 American Control Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACC.2013.6580307\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 American Control Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACC.2013.6580307","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper deals with direct data-driven design of model-reference controllers whose number of parameters is constrained. Input-output (I/O) sparse controllers are introduced and proposed as an alternative to low-order controller tuning. The optimal I/O sparse controller is shown to be never worse than the optimal low-order controller with the same number of parameters and a suited design procedure based on convex optimization is derived. The theoretical concepts are illustrated by means of a benchmark simulation example.