{"title":"变后退水平模型预测控制无穷水平性能的变化","authors":"X. Cai, Shaoyuan Li, Ning Li, Kang Li","doi":"10.3182/20130902-3-CN-3020.00194","DOIUrl":null,"url":null,"abstract":"Abstract This paper investigates variation of infinite horizon (IH) performance of Model Predictive Control (MPC) without constraints as the optimization horizon changes. By exploring properties of the Difference Riccati Equations (DRE), an upper bound and a lower bound of the ratio between variation of IH performance of MPC and finite horizon (FH) optimal cost are obtained. The result shows the dynamic behavior of IH performance of closed-loop MPC systems as the optimization horizon varies.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On Variation of Infinite Horizon Performance of Model Predictive Control with Varying Receding Horizon\",\"authors\":\"X. Cai, Shaoyuan Li, Ning Li, Kang Li\",\"doi\":\"10.3182/20130902-3-CN-3020.00194\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This paper investigates variation of infinite horizon (IH) performance of Model Predictive Control (MPC) without constraints as the optimization horizon changes. By exploring properties of the Difference Riccati Equations (DRE), an upper bound and a lower bound of the ratio between variation of IH performance of MPC and finite horizon (FH) optimal cost are obtained. The result shows the dynamic behavior of IH performance of closed-loop MPC systems as the optimization horizon varies.\",\"PeriodicalId\":90521,\"journal\":{\"name\":\"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3182/20130902-3-CN-3020.00194\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3182/20130902-3-CN-3020.00194","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On Variation of Infinite Horizon Performance of Model Predictive Control with Varying Receding Horizon
Abstract This paper investigates variation of infinite horizon (IH) performance of Model Predictive Control (MPC) without constraints as the optimization horizon changes. By exploring properties of the Difference Riccati Equations (DRE), an upper bound and a lower bound of the ratio between variation of IH performance of MPC and finite horizon (FH) optimal cost are obtained. The result shows the dynamic behavior of IH performance of closed-loop MPC systems as the optimization horizon varies.
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