{"title":"预测控制设计受到多个缺失的测量","authors":"Yuanyuan Zou, Y. Niu","doi":"10.1109/WCICA.2012.6358330","DOIUrl":null,"url":null,"abstract":"This paper investigates the problem of predictive control for control systems with multiple missing measurements. An extended stochastic model is introduced to describe and compensate missing data. The state feedback control scheme is designed to minimize an upper bound on the expected value of an infinite horizon quadratic performance objective at each sampling instant. It is shown that the present scheme can guarantee the stochastic stability of the closed-loop system.","PeriodicalId":114901,"journal":{"name":"Proceedings of the 10th World Congress on Intelligent Control and Automation","volume":"262 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Predictive control design subject to multiple missing measurements\",\"authors\":\"Yuanyuan Zou, Y. Niu\",\"doi\":\"10.1109/WCICA.2012.6358330\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper investigates the problem of predictive control for control systems with multiple missing measurements. An extended stochastic model is introduced to describe and compensate missing data. The state feedback control scheme is designed to minimize an upper bound on the expected value of an infinite horizon quadratic performance objective at each sampling instant. It is shown that the present scheme can guarantee the stochastic stability of the closed-loop system.\",\"PeriodicalId\":114901,\"journal\":{\"name\":\"Proceedings of the 10th World Congress on Intelligent Control and Automation\",\"volume\":\"262 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 10th World Congress on Intelligent Control and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WCICA.2012.6358330\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 10th World Congress on Intelligent Control and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCICA.2012.6358330","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predictive control design subject to multiple missing measurements
This paper investigates the problem of predictive control for control systems with multiple missing measurements. An extended stochastic model is introduced to describe and compensate missing data. The state feedback control scheme is designed to minimize an upper bound on the expected value of an infinite horizon quadratic performance objective at each sampling instant. It is shown that the present scheme can guarantee the stochastic stability of the closed-loop system.