Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2019 IEEE 58th Conference on Decision and Control (CDC) |
| Untertitel | Proceedings |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 1377-1382 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 978-1-7281-1398-2 |
| ISBN (Print) | 978-1-7281-1399-9 |
| Publikationsstatus | Veröffentlicht - Dez. 2019 |
| Veranstaltung | 2019 IEEE 58th Conference on Decision and Control (CDC) - Nice, France Dauer: 11 Dez. 2019 → 13 Dez. 2019 |
Publikationsreihe
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| Band | 2019-December |
| ISSN (Print) | 0743-1546 |
| ISSN (elektronisch) | 2576-2370 |
Abstract
In this paper, we present a dual adaptive model predictive control scheme for linear systems with single output subject to noise and parametric uncertainty. The proposed MPC approach incentives exploration of the unknown parameters by minimizing the expected output error, and hence results in a closed-loop behaviour as is typical in dual control. Parameters estimation results from a recursive least squares approach combined with a set-membership estimate. We show that the resulting dual adaptive MPC scheme ensures closed-loop practical stability and robust constraint satisfaction for state, input and output, despite parametric uncertainty and bounded output noise. In a numerical example, we show the practicality of the approach during set-point tracking, and we compare it with a certainty equivalence MPC scheme.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Steuerung und Optimierung
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Mathematik (insg.)
- Modellierung und Simulation
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- BibTex
- RIS
2019 IEEE 58th Conference on Decision and Control (CDC): Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. S. 1377-1382 9029693 (Proceedings of the IEEE Conference on Decision and Control; Band 2019-December).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung
}
TY - GEN
T1 - Dual Adaptive MPC for output tracking of linear systems
AU - Soloperto, Raffaele
AU - Köhler, Johannes
AU - Müller, Matthias A.
AU - Allgöwer, Frank
N1 - Funding information: The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS).
PY - 2019/12
Y1 - 2019/12
N2 - In this paper, we present a dual adaptive model predictive control scheme for linear systems with single output subject to noise and parametric uncertainty. The proposed MPC approach incentives exploration of the unknown parameters by minimizing the expected output error, and hence results in a closed-loop behaviour as is typical in dual control. Parameters estimation results from a recursive least squares approach combined with a set-membership estimate. We show that the resulting dual adaptive MPC scheme ensures closed-loop practical stability and robust constraint satisfaction for state, input and output, despite parametric uncertainty and bounded output noise. In a numerical example, we show the practicality of the approach during set-point tracking, and we compare it with a certainty equivalence MPC scheme.
AB - In this paper, we present a dual adaptive model predictive control scheme for linear systems with single output subject to noise and parametric uncertainty. The proposed MPC approach incentives exploration of the unknown parameters by minimizing the expected output error, and hence results in a closed-loop behaviour as is typical in dual control. Parameters estimation results from a recursive least squares approach combined with a set-membership estimate. We show that the resulting dual adaptive MPC scheme ensures closed-loop practical stability and robust constraint satisfaction for state, input and output, despite parametric uncertainty and bounded output noise. In a numerical example, we show the practicality of the approach during set-point tracking, and we compare it with a certainty equivalence MPC scheme.
UR - http://www.scopus.com/inward/record.url?scp=85082453746&partnerID=8YFLogxK
U2 - 10.1109/CDC40024.2019.9029693
DO - 10.1109/CDC40024.2019.9029693
M3 - Conference contribution
SN - 978-1-7281-1399-9
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1377
EP - 1382
BT - 2019 IEEE 58th Conference on Decision and Control (CDC)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE 58th Conference on Decision and Control (CDC)
Y2 - 11 December 2019 through 13 December 2019
ER -