Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 8853-8859 |
| Seitenumfang | 7 |
| Fachzeitschrift | International Federation of Automatic Control (IFAC) |
| Jahrgang | 53 |
| Ausgabenummer | 2 |
| Publikationsstatus | Veröffentlicht - 2020 |
Abstract
Physically motivated models of electric drive trains with coupled mechanics are ubiquitous in industry for control design, simulation, feed-forward, model-based fault diagnosis etc. Often, however, the effort of model building prohibits these model-based methods. In this paper an automated model selection strategy is proposed for dynamic simulation models that not only optimizes the accuracy of the fit but also ensures practical identifiability of model parameters during structural optimization. Practical identifiability is crucial for physically motivated, interpretable models as opposed to pure prediction and inference applications. Our approach extends structural optimization considering practical identifiability to nonlinear models. In spite of the nonlinearity, local and linear criteria are evaluated, the integrity of which is investigated exemplarily. The methods are validated experimentally on a stacker crane.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
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in: International Federation of Automatic Control (IFAC), Jahrgang 53, Nr. 2, 2020, S. 8853-8859.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Model Selection ensuring Practical Identifiability for Models of Electric Drives with Coupled Mechanics
AU - Tantau, Mathias
AU - Popp, Eduard
AU - Perner, Lars
AU - Wielitzka, Mark
AU - Ortmaier, Tobias
N1 - Funding information: This work was sponsored by the German Forschungsvereinigung Antriebstechnik e.V. (FVA) and the AiF Arbeitsgemeinschaft industrieller Forschungsvereinigungen”Otto von Guericke“e.V.
PY - 2020
Y1 - 2020
N2 - Physically motivated models of electric drive trains with coupled mechanics are ubiquitous in industry for control design, simulation, feed-forward, model-based fault diagnosis etc. Often, however, the effort of model building prohibits these model-based methods. In this paper an automated model selection strategy is proposed for dynamic simulation models that not only optimizes the accuracy of the fit but also ensures practical identifiability of model parameters during structural optimization. Practical identifiability is crucial for physically motivated, interpretable models as opposed to pure prediction and inference applications. Our approach extends structural optimization considering practical identifiability to nonlinear models. In spite of the nonlinearity, local and linear criteria are evaluated, the integrity of which is investigated exemplarily. The methods are validated experimentally on a stacker crane.
AB - Physically motivated models of electric drive trains with coupled mechanics are ubiquitous in industry for control design, simulation, feed-forward, model-based fault diagnosis etc. Often, however, the effort of model building prohibits these model-based methods. In this paper an automated model selection strategy is proposed for dynamic simulation models that not only optimizes the accuracy of the fit but also ensures practical identifiability of model parameters during structural optimization. Practical identifiability is crucial for physically motivated, interpretable models as opposed to pure prediction and inference applications. Our approach extends structural optimization considering practical identifiability to nonlinear models. In spite of the nonlinearity, local and linear criteria are evaluated, the integrity of which is investigated exemplarily. The methods are validated experimentally on a stacker crane.
KW - Model selection
KW - Practical identifiability
KW - Sensitivity analysis
KW - Structure and parameter identification
UR - http://www.scopus.com/inward/record.url?scp=85105096011&partnerID=8YFLogxK
U2 - 10.15488/10400
DO - 10.15488/10400
M3 - Conference article
VL - 53
SP - 8853
EP - 8859
JO - International Federation of Automatic Control (IFAC)
JF - International Federation of Automatic Control (IFAC)
IS - 2
ER -