Real-Time Feasibility of Data-Driven Predictive Control for Synchronous Motor Drives
dc.contributor.author
Carlet, Paolo Gherardo
dc.contributor.author
Favato, Andrea
dc.contributor.author
Torchio, Riccardo
dc.contributor.author
Toso, Francesco
dc.contributor.author
Bolognani, Saverio
dc.contributor.author
Dörfler, Florian
dc.date.accessioned
2023-07-28T10:28:21Z
dc.date.available
2022-11-04T13:21:47Z
dc.date.available
2022-11-04T14:20:43Z
dc.date.available
2022-11-16T08:12:22Z
dc.date.available
2023-07-28T10:28:21Z
dc.date.issued
2023-02
dc.identifier.issn
0885-8993
dc.identifier.issn
1941-0107
dc.identifier.other
10.1109/tpel.2022.3214760
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/579513
dc.identifier.doi
10.3929/ethz-b-000579513
dc.description.abstract
The data-driven control paradigm allows overcoming conventional troubles in the controller design related to model identifications procedures. Raw data are directly exploited in the control input selection by forcing the future plant dynamics to be coherent with previously collected samples. This paper focuses, in particular, on the data-enabled predictive control algorithm. A relevant disadvantage of this algorithm is the fact that the complexity of the online control program grows with the dimension of the data-set. This issue becomes particularly relevant when considering embedded applications such as the control of synchronous motor drives, characterized by challenging real-time constraints. This work proposes a systematic approach for dramatically reducing the complexity of such algorithms. Such methodology enables real-time feasibility of the constrained version of this control structure, which was previously precluded. Simulations and experimental results are provided to validate the method, considering the current control of an interior permanent magnet motor as test-case.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Data-enabled predictive control (DeePC)
en_US
dc.subject
model predictive control (MPC)
en_US
dc.subject
permanent magnet synchronous motor (PMSM)
en_US
dc.subject
proper orthogonal decomposition (POD)
en_US
dc.title
Real-Time Feasibility of Data-Driven Predictive Control for Synchronous Motor Drives
en_US
dc.type
Journal Article
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2022-10-14
ethz.journal.title
IEEE Transactions on Power Electronics
ethz.journal.volume
38
en_US
ethz.journal.issue
2
en_US
ethz.journal.abbreviated
IEEE trans. power electron.
ethz.pages.start
1672
en_US
ethz.pages.end
1682
en_US
ethz.size
11 p.
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
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
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::09478 - Dörfler, Florian / Dörfler, Florian
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::09478 - Dörfler, Florian / Dörfler, Florian
en_US
ethz.date.deposited
2022-11-04T13:21:47Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2022-11-16T08:12:23Z
ethz.rosetta.lastUpdated
2024-02-03T02:08:45Z
ethz.rosetta.versionExported
true
ethz.COinS
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