2024
DOI: 10.1002/cjce.25403
|View full text |Cite
|
Sign up to set email alerts
|

Data‐driven nonlinear state observation for controlled systems: A kernel method and its analysis

Moritz Woelk,
Wentao Tang

Abstract: This work proposes a data‐driven state observation algorithm for nonlinear dynamical systems, when the true state trajectory is not measurable and hence the states information needs to be reconstructed from input and output measurements. Such a reduction is formed by kernel canonical correlation analysis (KCCA), which (i) implicitly maps the available input–output data into a higher‐dimensional feature space, namely the reproducing kernel Hilbert space (RKHS); (ii) finds a projection of the past input–output d… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
references
References 27 publications
0
0
0
Order By: Relevance