{"title":"H∞ State estimation for stochastic state multiplicative systems","authors":"E. Gershon","doi":"10.1109/MED59994.2023.10185680","DOIUrl":null,"url":null,"abstract":"The problem of $H_{\\infty}$ state estimation is considered for uncertain polytopic linear discrete-time stochastic state-multiplicative systems. We first bring a unique version of the BRL for the latter systems which allows for vertex-dependent solution in the uncertain case. Following the BRL derivation, we solve the estimation problem for nominal systems which serves as a basis for extracting the filter parameters in the uncertain case. In both cases: the nominal and the uncertain cases, the filter parameters are extracted by a solving an LMI condition in the former case or a set of LMIs in the latter case, both of which depend on a minimal set of tuning parameters, thus greatly reduce the over-design. The theory presented is demonstrated by a numerical example.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"198 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 31st Mediterranean Conference on Control and Automation (MED)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MED59994.2023.10185680","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The problem of $H_{\infty}$ state estimation is considered for uncertain polytopic linear discrete-time stochastic state-multiplicative systems. We first bring a unique version of the BRL for the latter systems which allows for vertex-dependent solution in the uncertain case. Following the BRL derivation, we solve the estimation problem for nominal systems which serves as a basis for extracting the filter parameters in the uncertain case. In both cases: the nominal and the uncertain cases, the filter parameters are extracted by a solving an LMI condition in the former case or a set of LMIs in the latter case, both of which depend on a minimal set of tuning parameters, thus greatly reduce the over-design. The theory presented is demonstrated by a numerical example.