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dc.contributor.author
Fagiano, Lorenzo
dc.contributor.author
Schildbach, Georg
dc.contributor.author
Tanaskovic, Marko
dc.contributor.author
Morari, Manfred
dc.contributor.editor
Muñoz de la Peña, David
dc.contributor.editor
Limón, David
dc.date.accessioned
2021-07-28T07:52:31Z
dc.date.available
2017-06-12T05:10:25Z
dc.date.available
2018-09-11T08:53:55Z
dc.date.available
2021-07-28T07:52:31Z
dc.date.issued
2015-10
dc.identifier.issn
2405-8963
dc.identifier.other
10.1016/j.ifacol.2015.11.305
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/115861
dc.description.abstract
Two recent predictive control approaches for constrained systems subject to uncertainty are reviewed. The first one, named scenario MPC, is best suited for stochastic systems where a certain share of constraint violations is tolerated and rewarded. The approach is able to control precisely the share of violations that occur during closed loop operation, under quite general assumptions on the involved stochastic variables. The second technique, named adaptive MPC, is cast in a different framework, where the aim is to enforce robustly the system cnstraints and a stochastic characterization of the uncertainty is not required. The algorithm embeds a real-time set membership identification strategy that yields a refined set of unfalsified models at each time step, hence reducing the size of the model uncertainty and improving the closed loop performance over time. After recalling the main results pertaining to each approach, their applicability, strengths and weaknesses are discussed, as well as open issues that can be subject of future research.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Adaptive MPC
en_US
dc.subject
Chance Constraints
en_US
dc.subject
Model Predictive Control
en_US
dc.subject
Robust MPC
en_US
dc.subject
Scenario MPC
en_US
dc.subject
Scenario Optimization
en_US
dc.subject
Set Membership Identification
en_US
dc.subject
Soft Constraints
en_US
dc.subject
Stochastic MPC
en_US
dc.subject
Stochastic Systems
en_US
dc.title
Scenario and adaptive model predictive control of uncertain systems
en_US
dc.type
Conference Paper
dc.date.published
2015-12-17
ethz.book.title
5th IFAC Conference on Nonlinear Model Predictive Control, NMPC 2015. Proceedings
en_US
ethz.journal.title
IFAC-PapersOnLine
ethz.journal.volume
48
en_US
ethz.journal.issue
23
en_US
ethz.pages.start
352
en_US
ethz.pages.end
359
en_US
ethz.event
5th IFAC Conference on Nonlinear Model Predictive Control (NMPC 2015)
en_US
ethz.event.location
Seville, Spain
en_US
ethz.event.date
September 17–20, 2015
en_US
ethz.identifier.scopus
ethz.publication.place
Kidlington
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-06-12T05:15:17Z
ethz.source
ECIT
ethz.identifier.importid
imp5936545b7035414552
ethz.ecitpid
pub:177698
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2017-07-18T12:19:07Z
ethz.rosetta.lastUpdated
2022-03-29T10:45:52Z
ethz.rosetta.versionExported
true
ethz.COinS
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