Reference
F. Cordiano and B. De Schutter, "Scenario reduction with guarantees for
stochastic optimal control of linear systems,"
Proceedings of
the 2024 European Control Conference, Stockholm, Sweden, pp. 3502-3508,
June 2024.
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.
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BibTeX
@inproceedings{CorDeS:24-026,
author = {Cordiano, Francesco and De Schutter, Bart},
title = {Scenario Reduction with Guarantees for Stochastic Optimal
Control of Linear Systems},
booktitle = {Proceedings of the 2024 European Control Conference},
address = {Stockholm, Sweden},
pages = {3502--3508},
month = jun,
year = {2024}
}