Robust Optimal Control for Nonlinear Systems with Parametric Uncertainties via System Level Synthesis
dc.contributor.author
Leeman, Antoine
dc.contributor.author
Sieber, Jerome
dc.contributor.author
Bennani, Samir
dc.contributor.author
Zeilinger, Melanie N.
dc.date.accessioned
2024-03-07T08:20:02Z
dc.date.available
2023-09-13T12:39:22Z
dc.date.available
2023-09-13T14:36:40Z
dc.date.available
2024-03-07T08:20:02Z
dc.date.issued
2023
dc.identifier.isbn
979-8-3503-0124-3
en_US
dc.identifier.other
10.1109/CDC49753.2023.10383271
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/631300
dc.identifier.doi
10.3929/ethz-b-000631300
dc.description.abstract
This paper addresses the problem of optimally controlling nonlinear systems with norm-bounded disturbances and parametric uncertainties while robustly satisfying constraints. The proposed approach jointly optimizes a nominal nonlinear trajectory and an error feedback, requiring minimal offline design effort and offering low conservatism. This is achieved by decomposing the affine-in-the-parameter uncertain nonlinear system into a nominal nonlinear system and an uncertain linear time-varying system. Using this decomposition, we can apply established tools from system level synthesis to convexly over-bound all uncertainties in the nonlinear optimization problem. Moreover, it enables tight joint optimization of the linearization error bounds, parametric uncertainties bounds, nonlinear trajectory, and error feedback. With this novel controller parameterization, we can formulate a convex constraint to ensure robust performance guarantees for the nonlinear system. The presented method is relevant for numerous applications related to trajectory optimization, e.g., in robotics and aerospace engineering. We demonstrate the performance of the approach and its low conservatism through the simulation example of a post-capture satellite stabilization.
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
optimization
en_US
dc.subject
Nonlinear systems
en_US
dc.subject
optimal control
en_US
dc.subject
Robust control of nonlinear systems
en_US
dc.subject
Nonlinear predictive control
en_US
dc.title
Robust Optimal Control for Nonlinear Systems with Parametric Uncertainties via System Level Synthesis
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2024-01-19
ethz.book.title
2023 62nd IEEE Conference on Decision and Control (CDC)
en_US
ethz.pages.start
4784
en_US
ethz.pages.end
4791
en_US
ethz.size
8 p.
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
62nd IEEE Conference on Decision and Control (CDC 2023)
ethz.event.location
Singapore
ethz.event.date
December 13-15, 2023
en_US
ethz.identifier.wos
ethz.publication.place
Piscataway, NJ
en_US
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.relation.isSupplementedBy
https://gitlab.ethz.ch/ics/nonlinear-parametric-SLS
ethz.relation.isSupplementedBy
10.3929/ethz-b-000629589
ethz.relation.isNewVersionOf
10.48550/arXiv.2304.00752
ethz.date.deposited
2023-09-13T12:39:22Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2024-03-07T08:20:09Z
ethz.rosetta.lastUpdated
2024-03-07T08:20:09Z
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true
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true
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