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dc.contributor.author
Bertoni, Federica
dc.contributor.author
Giuliani, Matteo
dc.contributor.author
Castelletti, Andrea
dc.contributor.editor
Dochain, Denis
dc.contributor.editor
Henrion, Didier
dc.contributor.editor
Peaucelle, Dimitri
dc.date.accessioned
2021-07-28T05:39:44Z
dc.date.available
2017-10-29T02:38:36Z
dc.date.available
2017-11-29T13:45:52Z
dc.date.available
2018-08-31T14:08:46Z
dc.date.available
2021-07-28T05:39:44Z
dc.date.issued
2017-07
dc.identifier.issn
2405-8963
dc.identifier.other
10.1016/j.ifacol.2017.08.340
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/202010
dc.description.abstract
This study presents a novel approach called scenario-based Fitted Q-Iteration (sFQI) for controlling water reservoir systems under climate uncertainty. In these problems, robust control frameworks, based on worst-case realization, are usually adopted. Yet, these might be overly conservative. In this paper, we use sFQI to design adaptive control policies by enlarging the state space to include the space of the uncertain system’s parameters. This allows obtaining a control policy for any scenario in the uncertainty set with a single learning process. The method is demonstrated on a simplified model of the Lake Como system, a regulated lake operated for ensuring reliable water supply to downstream users. Numerical results show that the sFQI algorithm successfully identifies adaptive solutions to operate the system under different inflow scenarios, which outperform the control policy designed under historical conditions. Moreover, the sFQI policy generalizes over inflow scenarios not directly experienced during the policy design, thus alleviating the risk of mis-adaptation, namely the design of a solution fully adapted to a scenario that is different from the one that will actually realize.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
modelling
en_US
dc.subject
control under change
en_US
dc.subject
adaptive control
en_US
dc.subject
reinforcement learning
en_US
dc.subject
climate change
en_US
dc.subject
environmental engineering
en_US
dc.title
Scenario-based fitted Q-iteration for adaptive control of water reservoir systems under uncertainty
en_US
dc.type
Conference Paper
dc.date.published
2017-10-18
ethz.book.title
20th IFAC World Congress. Proceedings
en_US
ethz.journal.title
IFAC-PapersOnLine
ethz.journal.volume
50
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
3183
en_US
ethz.pages.end
3188
en_US
ethz.event
20th IFAC World Congress (IFAC 2017)
en_US
ethz.event.location
Toulouse, France
en_US
ethz.event.date
July 9-14, 2017
en_US
ethz.identifier.scopus
ethz.publication.place
Kidlington
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-10-29T02:40:55Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2017-11-29T13:46:11Z
ethz.rosetta.lastUpdated
2018-11-08T01:24:08Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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