Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2016 IEEE 55th Conference on Decision and Control, CDC 2016 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 2733-2738 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781509018376 |
| Publikationsstatus | Veröffentlicht - 27 Dez. 2016 |
| Extern publiziert | Ja |
| Veranstaltung | 55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, USA / Vereinigte Staaten Dauer: 12 Dez. 2016 → 14 Dez. 2016 |
Publikationsreihe
| Name | 2016 IEEE 55th Conference on Decision and Control, CDC 2016 |
|---|
Abstract
We propose a novel output feedback model predictive control scheme for linear discrete-time systems incorporating a set-valued estimator based on a fixed finite number of recent measurements. Recursive feasibility is established by basing predictions that are farther in the future on fewer measurements. The resulting optimization problem is convex with linear constraints. We demonstrate in a numerical example that the proposed model predictive control scheme allows an enlargement of the feasible set beyond what is possible with earlier schemes using linear estimators.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Entscheidungswissenschaften (insg.)
- Entscheidungswissenschaften (sonstige)
- Mathematik (insg.)
- Steuerung und Optimierung
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- BibTex
- RIS
2016 IEEE 55th Conference on Decision and Control, CDC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. S. 2733-2738 7798675 (2016 IEEE 55th Conference on Decision and Control, CDC 2016).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Enhancing output feedback MPC for linear discrete-time systems with set-valued moving horizon estimation
AU - Brunner, Florian D.
AU - Muller, Matthias A.
AU - Allgower, Frank
N1 - Publisher Copyright: © 2016 IEEE.
PY - 2016/12/27
Y1 - 2016/12/27
N2 - We propose a novel output feedback model predictive control scheme for linear discrete-time systems incorporating a set-valued estimator based on a fixed finite number of recent measurements. Recursive feasibility is established by basing predictions that are farther in the future on fewer measurements. The resulting optimization problem is convex with linear constraints. We demonstrate in a numerical example that the proposed model predictive control scheme allows an enlargement of the feasible set beyond what is possible with earlier schemes using linear estimators.
AB - We propose a novel output feedback model predictive control scheme for linear discrete-time systems incorporating a set-valued estimator based on a fixed finite number of recent measurements. Recursive feasibility is established by basing predictions that are farther in the future on fewer measurements. The resulting optimization problem is convex with linear constraints. We demonstrate in a numerical example that the proposed model predictive control scheme allows an enlargement of the feasible set beyond what is possible with earlier schemes using linear estimators.
UR - http://www.scopus.com/inward/record.url?scp=85010822214&partnerID=8YFLogxK
U2 - 10.1109/cdc.2016.7798675
DO - 10.1109/cdc.2016.7798675
M3 - Conference contribution
AN - SCOPUS:85010822214
T3 - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
SP - 2733
EP - 2738
BT - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 55th IEEE Conference on Decision and Control, CDC 2016
Y2 - 12 December 2016 through 14 December 2016
ER -