{"title":"Robust H/sub /spl infin// filtering for continuous-time linear systems with norm-bounded nonlinear uncertainties","authors":"M. R. Filho, C. J. Munaro","doi":"10.1109/CCA.2001.973947","DOIUrl":null,"url":null,"abstract":"This paper studies the problem of robust H/sub /spl infin// filtering of a class of continuous-time linear systems subject to parameter uncertainty. The class of uncertain systems is described by a state-space model with linear nominal parts and norm-bounded nonlinear uncertainties in the state equation. The proposed problem is the design of an asymptotically stable linear filter such that the L/sub 2/-induced gain from the noise signals to the estimation error is kept within a prescribed bound for all admissible parameter uncertainties.","PeriodicalId":365390,"journal":{"name":"Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCA.2001.973947","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper studies the problem of robust H/sub /spl infin// filtering of a class of continuous-time linear systems subject to parameter uncertainty. The class of uncertain systems is described by a state-space model with linear nominal parts and norm-bounded nonlinear uncertainties in the state equation. The proposed problem is the design of an asymptotically stable linear filter such that the L/sub 2/-induced gain from the noise signals to the estimation error is kept within a prescribed bound for all admissible parameter uncertainties.