Behavioral uncertainty quantification for data-driven control

Alberto Padoan*, Jeremy Coulson, Henk J. Van Waarde, John Lygeros, Florian Dorfler

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

2 Citaten (Scopus)
25 Downloads (Pure)


This paper explores the problem of uncertainty quantification in the behavioral setting for data-driven control. Building on classical ideas from robust control, the problem is regarded as that of selecting a metric which is best suited to a data-based description of uncertainties. Leveraging on Willems' fundamental lemma, restricted behaviors are viewed as subspaces of fixed dimension, which may be represented by data matrices. Consequently, metrics between restricted behaviors are defined as distances between points on the Grassmannian, i.e., the set of all subspaces of equal dimension in a given vector space. A new metric is defined on the set of restricted behaviors as a direct finite-time counterpart of the classical gap metric. The metric is shown to capture parametric uncertainty for the class of autoregressive (AR) models. Numerical simulations illustrate the value of the new metric with a data-driven mode recognition and control case study.

Originele taal-2English
Titel2022 IEEE 61st Conference on Decision and Control, CDC 2022
Aantal pagina's6
ISBN van elektronische versie978-1-6654-6761-2
ISBN van geprinte versie978-1-6654-6762-9
StatusPublished - 2022
Evenement61st IEEE Conference on Decision and Control, CDC 2022 - Cancun, Mexico
Duur: 6-dec.-20229-dec.-2022

Publicatie series

NaamProceedings of the IEEE Conference on Decision and Control
ISSN van geprinte versie0743-1546
ISSN van elektronische versie2576-2370


Conference61st IEEE Conference on Decision and Control, CDC 2022

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