Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | International Conference on Informatics in Control, Automation and Robotics |
| Herausgeber/-innen | Giuseppina Gini, Henk Nijmeijer, Wolfram Burgard, Dimitar P. Filev |
| Seiten | 605-615 |
| Seitenumfang | 11 |
| ISBN (elektronisch) | 978-989-758-585-2 |
| Publikationsstatus | Veröffentlicht - 2022 |
Publikationsreihe
| Name | Proceedings of the International Conference on Informatics in Control, Automation and Robotics |
|---|---|
| Band | 1 |
| ISSN (Print) | 2184-2809 |
Abstract
The challenge is that the resulting model-based controller is satisfactory only if the underlying model is appropriate.
Typically, a set of potential models is known a priori, but it is not known, which model should be used. So, the critical question in model-based controller tuning is that of model selection. Existing approaches for model selection are mostly based on maximizing accuracy, but there is no reason why the most accurate
model should also be the optimal model for control design. Given the overall aim to design a high-performance controller, in this paper the best model is considered as the one that has the potential to give a model-based controller the highest performance. The proposed method identifies parametric candidate models for control design. Then, a nonparametric model is used to predict the actual performance of the various controllers on the real system. A validation with two industry-like testbeds shows success of the method.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Signalverarbeitung
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
International Conference on Informatics in Control, Automation and Robotics. Hrsg. / Giuseppina Gini; Henk Nijmeijer; Wolfram Burgard; Dimitar P. Filev. 2022. S. 605-615 (Proceedings of the International Conference on Informatics in Control, Automation and Robotics; Band 1).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Control-relevant Model Selection for Multiple-Mass Systems
AU - Tantau, Mathias
AU - Jonsky, Torben
AU - Ziaukas, Zygimantas
AU - Jacob, Hans-Georg
N1 - Publisher Copyright: © 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Physically motivated parametric models are the basis of several techniques related to control design. Industrial model-based controller tuning methods include pole placement, symmetric optimum and damping optimum.The challenge is that the resulting model-based controller is satisfactory only if the underlying model is appropriate.Typically, a set of potential models is known a priori, but it is not known, which model should be used. So, the critical question in model-based controller tuning is that of model selection. Existing approaches for model selection are mostly based on maximizing accuracy, but there is no reason why the most accuratemodel should also be the optimal model for control design. Given the overall aim to design a high-performance controller, in this paper the best model is considered as the one that has the potential to give a model-based controller the highest performance. The proposed method identifies parametric candidate models for control design. Then, a nonparametric model is used to predict the actual performance of the various controllers on the real system. A validation with two industry-like testbeds shows success of the method.
AB - Physically motivated parametric models are the basis of several techniques related to control design. Industrial model-based controller tuning methods include pole placement, symmetric optimum and damping optimum.The challenge is that the resulting model-based controller is satisfactory only if the underlying model is appropriate.Typically, a set of potential models is known a priori, but it is not known, which model should be used. So, the critical question in model-based controller tuning is that of model selection. Existing approaches for model selection are mostly based on maximizing accuracy, but there is no reason why the most accuratemodel should also be the optimal model for control design. Given the overall aim to design a high-performance controller, in this paper the best model is considered as the one that has the potential to give a model-based controller the highest performance. The proposed method identifies parametric candidate models for control design. Then, a nonparametric model is used to predict the actual performance of the various controllers on the real system. A validation with two industry-like testbeds shows success of the method.
KW - Control-relevant Model Selection
KW - Model-based Control
KW - Modelless Simulation
KW - Multiple-mass Systems
KW - Non-parametric Models
UR - http://www.scopus.com/inward/record.url?scp=85175978505&partnerID=8YFLogxK
U2 - 10.15488/11990
DO - 10.15488/11990
M3 - Conference contribution
SN - 9789897585852
T3 - Proceedings of the International Conference on Informatics in Control, Automation and Robotics
SP - 605
EP - 615
BT - International Conference on Informatics in Control, Automation and Robotics
A2 - Gini, Giuseppina
A2 - Nijmeijer, Henk
A2 - Burgard, Wolfram
A2 - Filev, Dimitar P.
ER -