Details
| Original language | English |
|---|---|
| Pages (from-to) | 5598-5604 |
| Number of pages | 7 |
| Journal | IEEE Transactions on Automatic Control |
| Volume | 69 |
| Issue number | 8 |
| Early online date | 29 Feb 2024 |
| Publication status | Published - Aug 2024 |
Abstract
In this paper, a robust data-driven moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems is introduced. The scheme solely relies on offline collected data without employing any system identification step. We prove practical robust exponential stability for the setting where both the online measurements and the offline collected data are corrupted by non-vanishing and bounded noise. The behavior of the novel robust data-driven MHE scheme is illustrated by means of simulation examples and compared to a standard model-based MHE scheme, where the model is identified using the same offline data as for the data-driven MHE scheme.
Keywords
- Control systems, Data-driven state estimation, Linear systems, moving horizon estimation, Noise measurement, Observers, observers for linear systems, Phase measurement, state estimation, Time measurement, Trajectory, moving horizon estimation (MHE)
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Electrical and Electronic Engineering
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In: IEEE Transactions on Automatic Control, Vol. 69, No. 8, 08.2024, p. 5598-5604.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Robust Data-Driven Moving Horizon Estimation for Linear Discrete-Time Systems
AU - Wolff, Tobias M.
AU - Lopez, Victor G.
AU - Muller, Matthias A.
N1 - Publisher Copyright: © 1963-2012 IEEE.
PY - 2024/8
Y1 - 2024/8
N2 - In this paper, a robust data-driven moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems is introduced. The scheme solely relies on offline collected data without employing any system identification step. We prove practical robust exponential stability for the setting where both the online measurements and the offline collected data are corrupted by non-vanishing and bounded noise. The behavior of the novel robust data-driven MHE scheme is illustrated by means of simulation examples and compared to a standard model-based MHE scheme, where the model is identified using the same offline data as for the data-driven MHE scheme.
AB - In this paper, a robust data-driven moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems is introduced. The scheme solely relies on offline collected data without employing any system identification step. We prove practical robust exponential stability for the setting where both the online measurements and the offline collected data are corrupted by non-vanishing and bounded noise. The behavior of the novel robust data-driven MHE scheme is illustrated by means of simulation examples and compared to a standard model-based MHE scheme, where the model is identified using the same offline data as for the data-driven MHE scheme.
KW - Control systems
KW - Data-driven state estimation
KW - Linear systems
KW - moving horizon estimation
KW - Noise measurement
KW - Observers
KW - observers for linear systems
KW - Phase measurement
KW - state estimation
KW - Time measurement
KW - Trajectory
KW - moving horizon estimation (MHE)
UR - http://www.scopus.com/inward/record.url?scp=85186988387&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2210.09017
DO - 10.48550/arXiv.2210.09017
M3 - Article
AN - SCOPUS:85186988387
VL - 69
SP - 5598
EP - 5604
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
SN - 0018-9286
IS - 8
ER -