Sensitivity-based Model Reduction for In-Process Identification of Industrial Robots Inverse Dynamics

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Volkmann, B.; Kaczor, D.; Tantau, M.; Schappler, M.; Ortmaier, T.: Sensitivity-based Model Reduction for In-Process Identification of Industrial Robots Inverse Dynamics. In: 2020 IEEE International Conference on Mechatronics and Automation (ICMA). Piscataway, NJ, USA : IEEE, 2020, S. 912-919. DOI: http://dx.doi.org/10.1109/ICMA49215.2020.9233709

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/10355

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Sum total of downloads: 34




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Abstract: 
This paper presents a sensitivity-based approach for optimal model design and identification of the dynamics of a state-of-the-art industrial robot considering process-related restrictions. The possibility of parameter excitation for subsequent identification of the model parameters is severely limited due to restrictions imposed by the process environment, especially the limited available workspace. Without sufficient parameter excitation, a satisfactory quality of the full model identification cannot be achieved, since non-excited parameters cannot be identified correctly. Furthermore, optimal excitation requires time-consuming calculations and distinct experiments during which the robot is not available for daily operation. It is therefore of interest to use process-related trajectories instead of dedicated excitation trajectories, which is expected to deteriorate the identifiability of the model parameters. For this reason, the presented method uses a sensitivity-based approach allowing model order reduction in the identification process. The resulting model contains only those parameters excited by the excitation trajectory. For process-related trajectories this implies the model being limited to parameters relevant for the process. In experiments with a standard serial-link industrial robot controlled by standard industrial programmable logic control and servo inverters it is shown that the method produces significantly reduced models with a good measure of identifiability and quality.
License of this version: Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
Document Type: conferenceObject
Publishing status: acceptedVersion
Issue Date: 2020
Appears in Collections:Fakultät für Maschinenbau

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1 image of flag of Germany Germany 19 55.88%
2 image of flag of United States United States 5 14.71%
3 image of flag of Vietnam Vietnam 2 5.88%
4 image of flag of Japan Japan 2 5.88%
5 image of flag of United Kingdom United Kingdom 2 5.88%
6 image of flag of Netherlands Netherlands 1 2.94%
7 image of flag of Spain Spain 1 2.94%
8 image of flag of China China 1 2.94%
9 image of flag of Canada Canada 1 2.94%

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