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 | 1383-1388 |
| 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 robust adaptive model predictive control (MPC) scheme for linear systems subject to parametric uncertainty and additive disturbances. The proposed approach provides a computationally efficient formulation with theoretical guarantees (constraint satisfaction and stability), while allowing for reduced conservatism and improved performance due to online parameter adaptation. A moving window parameter set identification is used to compute a fixed complexity parameter set based on past data. Robust constraint satisfaction is achieved by using a computationally efficient tube based robust MPC method. The predicted cost function is based on a least mean squares point estimate, which ensures finite-gain Ⅎ2 stability of the closed loop. The overall algorithm has a fixed (user specified) computational complexity. We illustrate the applicability of the approach and the trade-off between conservatism and computational complexity using a numerical example.
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. 1383-1388 9028970 (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 - Linear robust adaptive model predictive control
T2 - 2019 IEEE 58th Conference on Decision and Control (CDC)
AU - Köhler, Johannes
AU - Andina, Elisa
AU - Soloperto, Raffaele
AU - Muller, Matthias A.
AU - Allgöwer, Frank
N1 - Funding information: This work was supported by the German Research Foundation under Grants GRK 2198/1, AL 316/12-1, and MU 3929/1-1, and by the International Max Planck Research School for Intelligent Systems (IMPRS-IS).
PY - 2019/12
Y1 - 2019/12
N2 - In this paper, we present a robust adaptive model predictive control (MPC) scheme for linear systems subject to parametric uncertainty and additive disturbances. The proposed approach provides a computationally efficient formulation with theoretical guarantees (constraint satisfaction and stability), while allowing for reduced conservatism and improved performance due to online parameter adaptation. A moving window parameter set identification is used to compute a fixed complexity parameter set based on past data. Robust constraint satisfaction is achieved by using a computationally efficient tube based robust MPC method. The predicted cost function is based on a least mean squares point estimate, which ensures finite-gain Ⅎ2 stability of the closed loop. The overall algorithm has a fixed (user specified) computational complexity. We illustrate the applicability of the approach and the trade-off between conservatism and computational complexity using a numerical example.
AB - In this paper, we present a robust adaptive model predictive control (MPC) scheme for linear systems subject to parametric uncertainty and additive disturbances. The proposed approach provides a computationally efficient formulation with theoretical guarantees (constraint satisfaction and stability), while allowing for reduced conservatism and improved performance due to online parameter adaptation. A moving window parameter set identification is used to compute a fixed complexity parameter set based on past data. Robust constraint satisfaction is achieved by using a computationally efficient tube based robust MPC method. The predicted cost function is based on a least mean squares point estimate, which ensures finite-gain Ⅎ2 stability of the closed loop. The overall algorithm has a fixed (user specified) computational complexity. We illustrate the applicability of the approach and the trade-off between conservatism and computational complexity using a numerical example.
UR - http://www.scopus.com/inward/record.url?scp=85082505288&partnerID=8YFLogxK
U2 - 10.1109/CDC40024.2019.9028970
DO - 10.1109/CDC40024.2019.9028970
M3 - Conference contribution
SN - 978-1-7281-1399-9
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1383
EP - 1388
BT - 2019 IEEE 58th Conference on Decision and Control (CDC)
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 December 2019 through 13 December 2019
ER -