Offline Uncertainty Sampling in Data-driven Stochastic MPC

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Teutsch, Johannes, Kerz, Sebastian, Brüdigam, Tim, Wollherr, Dirk, Leibold, Marion
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 06.04.2023
Subjects
Closed loops
Computer Science - Systems and Control
Constraints
Noise measurement
Noise prediction
Predictive control
Robust control
Sampling
Uncertainty
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ISSN2331-8422
DOI10.48550/arxiv.2304.03088

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Summary: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 closed-loop constraint satisfaction is shown. In a simulation example, we provide evidence for the improved control performance of the proposed control scheme in comparison to a purely robust data-driven predictive control approach.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2304.03088