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- Publisher Website: 10.1016/j.automatica.2016.07.009
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Article: Framework for state and unknown input estimation of linear time-varying systems
Title | Framework for state and unknown input estimation of linear time-varying systems |
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Authors | |
Keywords | State estimation Fault estimation Unknown input filtering Kalman filtering Double-Model Adaptive Estimation |
Issue Date | 2016 |
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 Identifier | http://hdl.handle.net/10722/288568 |
ISSN | 2023 Impact Factor: 4.8 2023 SCImago Journal Rankings: 3.502 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Lu, Peng | - |
dc.contributor.author | van Kampen, Erik Jan | - |
dc.contributor.author | de Visser, Cornelis C. | - |
dc.contributor.author | Chu, Qiping | - |
dc.date.accessioned | 2020-10-12T08:05:18Z | - |
dc.date.available | 2020-10-12T08:05:18Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Automatica, 2016, v. 73, p. 145-154 | - |
dc.identifier.issn | 0005-1098 | - |
dc.identifier.uri | http://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.language | eng | - |
dc.relation.ispartof | Automatica | - |
dc.subject | State estimation | - |
dc.subject | Fault estimation | - |
dc.subject | Unknown input filtering | - |
dc.subject | Kalman filtering | - |
dc.subject | Double-Model Adaptive Estimation | - |
dc.title | Framework for state and unknown input estimation of linear time-varying systems | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.automatica.2016.07.009 | - |
dc.identifier.scopus | eid_2-s2.0-84989831652 | - |
dc.identifier.volume | 73 | - |
dc.identifier.spage | 145 | - |
dc.identifier.epage | 154 | - |
dc.identifier.isi | WOS:000385327900019 | - |
dc.identifier.issnl | 0005-1098 | - |