Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2018 IEEE Conference on Decision and Control, CDC 2018 |
| Seiten | 3477-3482 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781538613955 |
| Publikationsstatus | Veröffentlicht - 2 Juli 2018 |
| Extern publiziert | Ja |
| Veranstaltung | 2018 IEEE Conference on Decision and Control (CDC) - Miami Beach, FL Dauer: 17 Dez. 2018 → 19 Dez. 2018 |
Publikationsreihe
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| Band | 2018-December |
| ISSN (Print) | 0743-1546 |
| ISSN (elektronisch) | 2576-2370 |
Abstract
In this paper, we consider optimization-based state estimation for general detectable nonlinear systems subject to unknown disturbances. The main contribution is a novel formulation of the cost function and a novel proof technique, which allows us (i) to ensure robust global exponential stability of the estimation error under a suitable exponential detectability condition and (ii) to overcome several of the drawbacks in the existing literature. In particular, we obtain improved estimates for the disturbance gains and the required minimal estimation horizon (which are independent of some maximum a priori disturbance bound), and provide a unified proof technique which can be used for both full information estimation and moving horizon estimation.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Steuerung und Optimierung
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Mathematik (insg.)
- Modellierung und Simulation
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2018 IEEE Conference on Decision and Control, CDC 2018. 2018. S. 3477-3482 8619617 (Proceedings of the IEEE Conference on Decision and Control; Band 2018-December).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung
}
TY - GEN
T1 - Robust Global Exponential Stability for Moving Horizon Estimation
AU - Knüfer, Sven
AU - Muller, Matthias A.
N1 - Funding information: The authors are indebted to the Baden-Württemberg Stiftung for the financial support of this research project by the Elite Programme for Postdocs.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In this paper, we consider optimization-based state estimation for general detectable nonlinear systems subject to unknown disturbances. The main contribution is a novel formulation of the cost function and a novel proof technique, which allows us (i) to ensure robust global exponential stability of the estimation error under a suitable exponential detectability condition and (ii) to overcome several of the drawbacks in the existing literature. In particular, we obtain improved estimates for the disturbance gains and the required minimal estimation horizon (which are independent of some maximum a priori disturbance bound), and provide a unified proof technique which can be used for both full information estimation and moving horizon estimation.
AB - In this paper, we consider optimization-based state estimation for general detectable nonlinear systems subject to unknown disturbances. The main contribution is a novel formulation of the cost function and a novel proof technique, which allows us (i) to ensure robust global exponential stability of the estimation error under a suitable exponential detectability condition and (ii) to overcome several of the drawbacks in the existing literature. In particular, we obtain improved estimates for the disturbance gains and the required minimal estimation horizon (which are independent of some maximum a priori disturbance bound), and provide a unified proof technique which can be used for both full information estimation and moving horizon estimation.
UR - http://www.scopus.com/inward/record.url?scp=85062170038&partnerID=8YFLogxK
U2 - 10.1109/CDC.2018.8619617
DO - 10.1109/CDC.2018.8619617
M3 - Conference contribution
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 3477
EP - 3482
BT - 2018 IEEE Conference on Decision and Control, CDC 2018
T2 - 2018 IEEE Conference on Decision and Control (CDC)
Y2 - 17 December 2018 through 19 December 2018
ER -