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dc.contributor.author
Carron, Andrea
dc.contributor.author
Sieber, Jerome
dc.contributor.author
Zeilinger, Melanie
dc.date.accessioned
2021-11-05T10:26:36Z
dc.date.available
2021-11-05T10:26:36Z
dc.date.issued
2021
dc.identifier.issn
2405-8963
dc.identifier.other
10.1016/j.ifacol.2021.06.067
dc.identifier.uri
http://hdl.handle.net/20.500.11850/513889.1
dc.identifier.doi
10.3929/ethz-b-000454950
dc.description.abstract
In large-scale networks of uncertain dynamical systems, where communication is limited and there is a strong interaction among subsystems, learning local models and control policies offers great potential for designing high-performance controllers. At the same time, the lack of safety guarantees, here considered in the form of constraint satisfaction, prevents the use of data-driven techniques to safety-critical distributed systems. This paper presents a safety framework that guarantees constraint satisfaction for uncertain distributed systems while learning. The framework considers linear systems with coupling in the dynamics and subject to bounded parametric uncertainty, and makes use of robust invariance to guarantee safety. In particular, a robust non-convex invariant set, given by the union of multiple ellipsoidal invariant sets, and a nonlinear backup control law, given by the combination of multiple stabilizing linear feedbacks, are computed offline. In presence of unsafe inputs, the safety framework applies the backup control law, preventing the system to violate the constraints. As the robust invariant set and the backup stabilizing controller are computed offline, the online operations reduce to simple function evaluations, which enables the use of the proposed framework on systems with limited computational resources. The capabilities of the safety framework are illustrated by three numerical examples.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Networked Control Systems
en_US
dc.subject
Linear Systems
en_US
dc.subject
safe learning
en_US
dc.title
Distributed Safe Learning using an Invariance-based Safety Framework
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
54
ethz.journal.issue
9
ethz.pages.start
95
ethz.pages.end
102
ethz.size
8 p. submitted version
en_US
ethz.version.deposit
submittedVersion
en_US
ethz.event
24th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2020)
en_US
ethz.event.location
Cambridge, United Kingdom
en_US
ethz.event.date
August 23–27, 2021
en_US
ethz.notes
Cancellation of the postponed 24th MTNS which was due to be held in Cambridge in August 2021 due to the Corona virus (COVID-19).
en_US
ethz.identifier.scopus
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02619 - Inst. Dynam. Syst. u. Regelungstechnik / Inst. Dynamic Systems and Control::09563 - Zeilinger, Melanie / Zeilinger, Melanie
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02619 - Inst. Dynam. Syst. u. Regelungstechnik / Inst. Dynamic Systems and Control::09563 - Zeilinger, Melanie / Zeilinger, Melanie
en_US
ethz.date.deposited
2020-12-07T09:25:21Z
ethz.source
SCOPUS
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-11-05T10:26:45Z
ethz.rosetta.lastUpdated
2021-11-05T10:26:45Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/513750
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/454950
ethz.COinS
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