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Titel:

Offline Uncertainty Sampling in Data-driven Stochastic MPC

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Vortrag / Präsentation
Autor(en):
Teutsch, Johannes; Kerz, Sebastian; Brüdigam, Tim; Wollherr, Dirk; Leibold, Marion
Seitenangaben Beitrag:
7
Abstract:
In this work, we exploit an offline-sampling based strategy for the constrained data-driven predictive control of an unknown linear system subject to random measurement noise. The strategy uses only past measured, potentially noisy data in a non-parametric system representation and does not require any prior model identification. The approximation of chance constraints using uncertainty sampling leads to efficient constraint tightening. Under mild assumptions, robust recursive feasibility and cl...     »
Stichworte:
Data-driven optimal control, Stochastic optimal control problems, Data-based control, Predictive control, Linear systems, Uncertain systems, Constrained control
Kongress- / Buchtitel:
IFAC World Congress 2023
Jahr:
2023
CC-Lizenz:
by-nc-nd, http://creativecommons.org/licenses/by-nc-nd/4.0
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