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Comparison of Data-Driven Modeling and Identification Approaches for a Self-Balancing Vehicle

  • This paper gives a systematic comparison of different state–of–the–art modeling approaches and the corresponding parameter identification processes for a self–balancing vehicle. In detail, a nonlinear grey box model, its extension to consider friction effects, a parametric black box model based on regression neural networks, and a hybrid approach are presented. The parameters of the models are identified by solving a nonlinear least squares problem. The training, validation, and test datasets are collected in full–scale experiments using a self–balancing vehicle. The performance of the different models used for ego–motion prediction are compared in full–scale scenarios, as well. The investigated model architectures can be used to improve both, simulation environments and model–based controller design. This paper shows the upsides and downsides arising from using the different modeling approaches. Videos showing the self–balancing vehicle in action are available at: https://tinyurl.com/mvn8j7vf22nd

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Metadaten
Author:Hannes HomburgerORCiD, Stefan WirtensohnORCiD, Moritz DiehlORCiD, Johannes ReuterORCiD
DOI:https://doi.org/10.1016/j.ifacol.2023.10.611
ISSN:2405-8963
Parent Title (English):22nd World Congress of the International Federation of Automatic Control, 9–14 July 2023, Yokohama, Japan; (IFAC-PapersOnLine)
Volume:56
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Conference Proceeding
Language:English
Year of Publication:2023
Release Date:2023/12/18
Tag:Nonlinear system identification; Mechatronic systems; Grey box modeling; Nonlinear least squares problem; Machine learning; Statistical data analysis
Issue:2
First Page:6839
Last Page:6844
Open Access?:Ja
Relevance:Peer reviewed nach anderen Listungen (mit Nachweis zum Peer Review Verfahren)
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International