Abstract
Scenario reduction algorithms can be an effective means to provide a tractable description of the uncertainty in optimal control problems. However, they might significantly compromise the performance of the controlled system. In this paper, we propose a method to compensate for the effect of scenario reduction on stochastic optimal control problems for chance-constrained linear systems with additive uncertainty. We consider a setting in which the uncertainty has a discrete distribution, where the number of possible realizations is large. We then propose a reduction algorithm with a problem-dependent loss function, and we define sufficient conditions on the stochastic optimal control problem to ensure out-of-sample guarantees (i.e., against the original distribution of the uncertainty) for the controlled system in terms of performance and chance constraint satisfaction. Finally, we demonstrate the effectiveness of the approach on a numerical example.
Original language | English |
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Title of host publication | Proceedings of the European Control Conference, ECC 2024 |
Publisher | IEEE |
Pages | 3502-3508 |
Number of pages | 7 |
ISBN (Electronic) | 978-3-9071-4410-7 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 European Control Conference, ECC 2024 - Stockholm, Sweden Duration: 25 Jun 2024 → 28 Jun 2024 |
Conference
Conference | 2024 European Control Conference, ECC 2024 |
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Country/Territory | Sweden |
City | Stockholm |
Period | 25/06/24 → 28/06/24 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.