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.
Bibtex entry:
@inproceedings{CorDeS:24-026,