Daiane C. Bortolin, Elizandra K. Odorico, M. Terra
{"title":"Robust Linear Quadratic Regulator for Uncertain Linear Discrete-Time Systems with Delay in the States: an augmented system approach","authors":"Daiane C. Bortolin, Elizandra K. Odorico, M. Terra","doi":"10.23919/ECC.2018.8550532","DOIUrl":null,"url":null,"abstract":"In this paper we deal with the regulation problem for a class of uncertain discrete-time systems with known constant delays in the states. Uncertainties are assumed norm-bounded and affect all parametric matrices of the system. Applying the lifting method, the delayed system is transformed into an augmented delay-free system. Then, the control law is obtained from combination of penalty functions and robust regularized least-squares problem, when there exist uncertainties in the data. The solution provided is given in terms of augmented Riccati equations presented in a framework given by an array of matrices.","PeriodicalId":222660,"journal":{"name":"2018 European Control Conference (ECC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 European Control Conference (ECC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ECC.2018.8550532","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper we deal with the regulation problem for a class of uncertain discrete-time systems with known constant delays in the states. Uncertainties are assumed norm-bounded and affect all parametric matrices of the system. Applying the lifting method, the delayed system is transformed into an augmented delay-free system. Then, the control law is obtained from combination of penalty functions and robust regularized least-squares problem, when there exist uncertainties in the data. The solution provided is given in terms of augmented Riccati equations presented in a framework given by an array of matrices.