Details
| Original language | English |
|---|---|
| Title of host publication | 2022 European Control Conference (ECC) |
| Place of Publication | [Piscataway, NJ] |
| Pages | 1778-1783 |
| Number of pages | 6 |
| ISBN (electronic) | 978-3-9071-4407-7 |
| Publication status | Published - 2022 |
| Event | 2022 European Control Conference (ECC) - London, United Kingdom (UK) Duration: 12 Jul 2022 → 15 Jul 2022 |
Abstract
This paper introduces a data-based moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems. The scheme solely relies on collected data with-out employing any system identification step. Robust global exponential stability of the data-based MHE is proven under standard assumptions for the case where the online output measurements are corrupted by some non-vanishing measurement noise. A simulation example illustrates the behavior of the data-based MHE scheme.
ASJC Scopus subject areas
- Decision Sciences(all)
- Information Systems and Management
- Computer Science(all)
- Artificial Intelligence
- Mathematics(all)
- Control and Optimization
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Networks and Communications
- Mathematics(all)
- Modelling and Simulation
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2022 European Control Conference (ECC). [Piscataway, NJ], 2022. p. 1778-1783.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Data-Based Moving Horizon Estimation for Linear Discrete-Time Systems
AU - Wolff, Tobias M.
AU - Lopez, Victor G.
AU - Müller, Matthias A.
N1 - Funding Information: *This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 948679). 1Leibniz University Hannover, Institute of Control, 30167 Hannover, Germany. E-mail:
PY - 2022
Y1 - 2022
N2 - This paper introduces a data-based moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems. The scheme solely relies on collected data with-out employing any system identification step. Robust global exponential stability of the data-based MHE is proven under standard assumptions for the case where the online output measurements are corrupted by some non-vanishing measurement noise. A simulation example illustrates the behavior of the data-based MHE scheme.
AB - This paper introduces a data-based moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems. The scheme solely relies on collected data with-out employing any system identification step. Robust global exponential stability of the data-based MHE is proven under standard assumptions for the case where the online output measurements are corrupted by some non-vanishing measurement noise. A simulation example illustrates the behavior of the data-based MHE scheme.
UR - http://www.scopus.com/inward/record.url?scp=85136691216&partnerID=8YFLogxK
U2 - 10.23919/ECC55457.2022.9838331
DO - 10.23919/ECC55457.2022.9838331
M3 - Conference contribution
SN - 978-1-6654-9733-6
SP - 1778
EP - 1783
BT - 2022 European Control Conference (ECC)
CY - [Piscataway, NJ]
T2 - 2022 European Control Conference (ECC)
Y2 - 12 July 2022 through 15 July 2022
ER -