File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Framework for state and unknown input estimation of linear time-varying systems

TitleFramework for state and unknown input estimation of linear time-varying systems
Authors
KeywordsState estimation
Fault estimation
Unknown input filtering
Kalman filtering
Double-Model Adaptive Estimation
Issue Date2016
Citation
Automatica, 2016, v. 73, p. 145-154 How to Cite?
Abstract© 2016 Elsevier Ltd The design of unknown-input decoupled observers and filters requires the assumption of an existence condition in the literature. This paper addresses an unknown input filtering problem where the existence condition is not satisfied. Instead of designing a traditional unknown input decoupled filter, a Double-Model Adaptive Estimation approach is extended to solve the unknown input filtering problem. It is proved that the state and the unknown inputs can be estimated and decoupled using the extended Double-Model Adaptive Estimation approach without satisfying the existence condition. Numerical examples are presented in which the performance of the proposed approach is compared to methods from literature.
Persistent Identifierhttp://hdl.handle.net/10722/288568
ISSN
2022 Impact Factor: 6.4
2020 SCImago Journal Rankings: 3.132
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLu, Peng-
dc.contributor.authorvan Kampen, Erik Jan-
dc.contributor.authorde Visser, Cornelis C.-
dc.contributor.authorChu, Qiping-
dc.date.accessioned2020-10-12T08:05:18Z-
dc.date.available2020-10-12T08:05:18Z-
dc.date.issued2016-
dc.identifier.citationAutomatica, 2016, v. 73, p. 145-154-
dc.identifier.issn0005-1098-
dc.identifier.urihttp://hdl.handle.net/10722/288568-
dc.description.abstract© 2016 Elsevier Ltd The design of unknown-input decoupled observers and filters requires the assumption of an existence condition in the literature. This paper addresses an unknown input filtering problem where the existence condition is not satisfied. Instead of designing a traditional unknown input decoupled filter, a Double-Model Adaptive Estimation approach is extended to solve the unknown input filtering problem. It is proved that the state and the unknown inputs can be estimated and decoupled using the extended Double-Model Adaptive Estimation approach without satisfying the existence condition. Numerical examples are presented in which the performance of the proposed approach is compared to methods from literature.-
dc.languageeng-
dc.relation.ispartofAutomatica-
dc.subjectState estimation-
dc.subjectFault estimation-
dc.subjectUnknown input filtering-
dc.subjectKalman filtering-
dc.subjectDouble-Model Adaptive Estimation-
dc.titleFramework for state and unknown input estimation of linear time-varying systems-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.automatica.2016.07.009-
dc.identifier.scopuseid_2-s2.0-84989831652-
dc.identifier.volume73-
dc.identifier.spage145-
dc.identifier.epage154-
dc.identifier.isiWOS:000385327900019-
dc.identifier.issnl0005-1098-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats