{"title":"用于迭代学习控制的干扰衰减","authors":"K. Gałkowski, P. Dabkowski, E. Rogers","doi":"10.1109/ACC.2014.6858745","DOIUrl":null,"url":null,"abstract":"Previous research has shown that repetitive processes, a class of 2D systems, can be used to design linear model based iterative learning control laws for convergence and transient performance, with supporting experimental benchmarking. In many applications attenuation of disturbances acting on the plant signals will also be required. The new results in this paper are control law design algorithms for this problem with disturbance attenuation measured by an ℋ∞ norm.","PeriodicalId":369729,"journal":{"name":"2014 American Control Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ℋ℞ based disturbance attenuation for iterative learning control\",\"authors\":\"K. Gałkowski, P. Dabkowski, E. Rogers\",\"doi\":\"10.1109/ACC.2014.6858745\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Previous research has shown that repetitive processes, a class of 2D systems, can be used to design linear model based iterative learning control laws for convergence and transient performance, with supporting experimental benchmarking. In many applications attenuation of disturbances acting on the plant signals will also be required. The new results in this paper are control law design algorithms for this problem with disturbance attenuation measured by an ℋ∞ norm.\",\"PeriodicalId\":369729,\"journal\":{\"name\":\"2014 American Control Conference\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 American Control Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACC.2014.6858745\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 American Control Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACC.2014.6858745","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ℋ℞ based disturbance attenuation for iterative learning control
Previous research has shown that repetitive processes, a class of 2D systems, can be used to design linear model based iterative learning control laws for convergence and transient performance, with supporting experimental benchmarking. In many applications attenuation of disturbances acting on the plant signals will also be required. The new results in this paper are control law design algorithms for this problem with disturbance attenuation measured by an ℋ∞ norm.
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