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dc.contributor.author
Parsi, Anil
dc.contributor.author
Iannelli, Andrea
dc.contributor.author
Smith, Roy
dc.date.accessioned
2022-11-25T06:29:37Z
dc.date.available
2022-11-24T17:17:58Z
dc.date.available
2022-11-25T06:29:37Z
dc.date.issued
2022
dc.identifier.issn
1049-8923
dc.identifier.issn
1099-1239
dc.identifier.other
10.1002/rnc.6485
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/582916
dc.identifier.doi
10.3929/ethz-b-000582916
dc.description.abstract
This work proposes a novel robust model predictive control (MPC) algorithm for linear systems affected by dynamic model uncertainty and exogenous disturbances. The uncertainty is modeled using a linear fractional perturbation structure with a time-varying perturbation matrix, enabling the algorithm to be applied to a large model class. The MPC controller constructs a state tube as a sequence of parameterized ellipsoidal sets to bound the state trajectories of the system. The proposed approach results in a semidefinite program to be solved online, whose size scales linearly with the order of the system. The design of the state tube is formulated as an offline optimization problem, which offers flexibility to impose desirable features such as robust invariance on the terminal set. This contrasts with most existing tube MPC strategies using polytopic sets in the state tube, which are difficult to design and whose complexity grows combinatorially with the system order. The algorithm guarantees constraint satisfaction, recursive feasibility, and stability of the closed loop. The advantages of the algorithm are demonstrated using two simulation studies.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Wiley
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
CONTROL ENGINEERING THEORY (ELECTRICAL ENGINEERING)
en_US
dc.subject
Model predictive control (MPC)
en_US
dc.title
Scalable tube model predictive control of uncertain linear systems using ellipsoidal sets
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2022-11-21
ethz.journal.title
International Journal of Robust and Nonlinear Control
ethz.journal.abbreviated
Int. j. robust nonlinear control
ethz.size
22 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Modeling, Identification and Control of Periodic Systems in Energy Applications
en_US
ethz.identifier.wos
ethz.publication.place
s.l.
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02650 - Institut für Automatik / Automatic Control Laboratory::08814 - Smith, Roy (Tit.-Prof.)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02650 - Institut für Automatik / Automatic Control Laboratory::08814 - Smith, Roy (Tit.-Prof.)
en_US
ethz.grant.agreementno
178890
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Projekte MINT
ethz.relation.isSupplementedBy
10.3929/ethz-b-000559871
ethz.date.deposited
2022-11-24T17:17:58Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.exportRequired
true
ethz.COinS
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