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  4. Towards data-driven stochastic predictive control
 
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Towards data-driven stochastic predictive control

Citation Link: https://doi.org/10.15480/882.9182
Publikationstyp
Journal Article
Date Issued
2025-05-10
Sprache
English
Author(s)
Pan, Guanru
Ou, Ruchuan  
Faulwasser, Timm  
TORE-DOI
10.15480/882.9182
TORE-URI
https://hdl.handle.net/11420/45641
Journal
International journal of robust and nonlinear control  
Volume
35
Issue
7
Start Page
2588
End Page
2610
Citation
International Journal of Robust and Nonlinear Control 35 (7): 2588-2610 (2025)
Publisher DOI
10.1002/rnc.6812
Scopus ID
2-s2.0-105001653479
Publisher
Wiley
Data-driven predictive control based on the fundamental lemma by Willems et al. is frequently considered for deterministic LTI systems subject to measurement noise. However, little has been done on data-driven stochastic control. In this paper, we propose a data-driven stochastic predictive control scheme for LTI systems subject to possibly unbounded additive process disturbances. Based on a stochastic extension of the fundamental lemma and leveraging polynomial chaos expansions, we construct a data-driven surrogate optimal control problem (OCP). Moreover, combined with an online selection strategy of the initial condition of the OCP, we provide sufficient conditions for recursive feasibility and for stability of the proposed data-driven predictive control scheme. Finally, two numerical examples illustrate the efficacy and closed-loop properties of the proposed scheme for process disturbances governed by different distributions.
Subjects
closed-loop properties
data-driven system representation
polynomial chaos expansion
stochastic model predictive control
DDC Class
621: Applied Physics
510: Mathematics
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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