Details
| Original language | English |
|---|---|
| Pages (from-to) | 1-16 |
| Number of pages | 16 |
| Journal | IEEE Transactions on Automatic Control |
| Publication status | Published - 4 Jul 2024 |
Abstract
Keywords
- Biomedical measurement, Dynamical systems, Kernel, Linear systems, Noise measurement, Time series analysis, Trajectory
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Science Applications
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In: IEEE Transactions on Automatic Control, 04.07.2024, p. 1-16.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Data-based System Representations from Irregularly Measured Data
AU - Alsalti, Mohammad Salahaldeen Ahmad
AU - Markovsky, Ivan
AU - Lopez Mejia, Victor Gabriel
AU - Müller, Matthias A.
N1 - Publisher Copyright: IEEE
PY - 2024/7/4
Y1 - 2024/7/4
N2 - Non-parametric representations of dynamical systems based on the image of a Hankel matrix of data are extensively used for data-driven control. However, if samples of data are missing, obtaining such representations becomes a difficult task. By exploiting the kernel structure of Hankel matrices of irregularly measured data generated by a linear time-invariant system, we provide computational methods for which any complete finite-length behavior of the system can be obtained. For the special case of periodically missing outputs, we provide conditions on the input such that the former result is guaranteed. In the presence of noise in the data, our method returns an approximate finite-length behavior of the system. We illustrate our result with several examples, including its use for approximate data completion in real-world applications and compare it to alternative methods.
AB - Non-parametric representations of dynamical systems based on the image of a Hankel matrix of data are extensively used for data-driven control. However, if samples of data are missing, obtaining such representations becomes a difficult task. By exploiting the kernel structure of Hankel matrices of irregularly measured data generated by a linear time-invariant system, we provide computational methods for which any complete finite-length behavior of the system can be obtained. For the special case of periodically missing outputs, we provide conditions on the input such that the former result is guaranteed. In the presence of noise in the data, our method returns an approximate finite-length behavior of the system. We illustrate our result with several examples, including its use for approximate data completion in real-world applications and compare it to alternative methods.
KW - Biomedical measurement
KW - Dynamical systems
KW - Kernel
KW - Linear systems
KW - Noise measurement
KW - Time series analysis
KW - Trajectory
UR - http://www.scopus.com/inward/record.url?scp=85197481941&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2307.11589
DO - 10.48550/arXiv.2307.11589
M3 - Article
SP - 1
EP - 16
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
SN - 0018-9286
ER -