Model Selection ensuring Practical Identifiability for Models of Electric Drives with Coupled Mechanics

Download statistics - Document (COUNTER):

Tantau, M.; Popp, E.; Perner, L.; Wielitzka, M.; Ortmaier, T.: Model Selection ensuring Practical Identifiability for Models of Electric Drives with Coupled Mechanics. In: IFAC-PapersOnLine 53 (2020), Nr. 2, S. 8853-8859. DOI: https://doi.org/10.1016/j.ifacol.2020.12.1400

Repository version

To cite the version in the repository, please use this identifier: https://doi.org/10.15488/10400

Selected time period:

year: 
month: 

Sum total of downloads: 46




Thumbnail
Abstract: 
Physically motivated models of electric drive trains with coupled mechanics areubiquitous 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.
License of this version: CC BY-NC-ND 4.0 Unported
Document Type: article
Publishing status: acceptedVersion
Issue Date: 2020-07-11
Appears in Collections:Fakultät für Maschinenbau

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 30 65.22%
2 image of flag of Russian Federation Russian Federation 5 10.87%
3 image of flag of United States United States 3 6.52%
4 image of flag of Czech Republic Czech Republic 3 6.52%
5 image of flag of No geo information available No geo information available 1 2.17%
6 image of flag of Thailand Thailand 1 2.17%
7 image of flag of Netherlands Netherlands 1 2.17%
8 image of flag of United Kingdom United Kingdom 1 2.17%
9 image of flag of China China 1 2.17%

Further download figures and rankings:


Hinweis

Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.

Search the repository


Browse