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dc.contributor.author
Brouillon, Jean-Sébastien
dc.contributor.author
Dörfler, Florian
dc.contributor.author
Ferrari-Trecate, Giancarlo
dc.contributor.editor
Ishii, Hideaki
dc.contributor.editor
Ebihara, Yoshio
dc.contributor.editor
Imura, Jun-ichi
dc.contributor.editor
Yamakita, Masaki
dc.date.accessioned
2024-02-22T13:16:44Z
dc.date.available
2024-02-21T09:08:46Z
dc.date.available
2024-02-22T13:16:44Z
dc.date.issued
2023-11-22
dc.identifier.issn
2405-8963
dc.identifier.other
10.1016/j.ifacol.2023.10.1345
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/660769
dc.identifier.doi
10.3929/ethz-b-000660769
dc.description.abstract
Kalman and H∞ filters, the most popular paradigms for linear state estimation, are designed for very specific specific noise and disturbance patterns, which may not appear in practice. State observers based on the minimization of regret measures are a promising alternative, as they aim to adapt to recognizable patterns in the estimation error. In this paper, we show that the regret minimization problem for finite horizon estimation can be cast into a simple convex optimization problem. For this purpose, we first rewrite linear time-varying system dynamics using a novel system level synthesis parametrization for state estimation, capable of handling both disturbance and measurement noise. We then provide a tractable formulation for the minimization of regret based on semi-definite programming. Both contributions make the minimal regret observer design easily implementable in practice. Finally, numerical experiments show that the computed observer can significantly outperform both H₂ and H∞ filters.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
minimal regret
en_US
dc.subject
observer design
en_US
dc.subject
state estimation
en_US
dc.title
Minimal regret state estimation of time-varying systems
en_US
dc.type
Conference Paper
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
ethz.journal.title
IFAC-PapersOnLine
ethz.journal.volume
56
en_US
ethz.journal.issue
2
en_US
ethz.pages.start
2595
en_US
ethz.pages.end
2600
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.event
22nd IFAC World Congress 2023
en_US
ethz.event.location
Yokohama, Japan
en_US
ethz.event.date
July 9-14, 2023
en_US
ethz.grant
NCCR Automation (phase I)
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Frankfurt
en_US
ethz.publication.status
published
en_US
ethz.grant.agreementno
180545
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
NCCR full proposal
ethz.date.deposited
2024-02-21T09:08:52Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2024-02-22T13:16:45Z
ethz.rosetta.lastUpdated
2024-02-22T13:16:45Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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