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

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dc.identifier.uri Volkmann, Björn eng Kaczor, Daniel eng Tantau, Mathias eng Schappler, Moritz eng Ortmaier, Tobias eng 2021-02-01T06:12:41Z 2020
dc.identifier.citation 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: eng
dc.description.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. eng
dc.language.iso eng eng
dc.publisher Piscataway, NJ, USA : IEEE 2020
dc.rights Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden. eng
dc.subject Model order reduction eng
dc.subject Sensitivity eng
dc.subject Identification eng
dc.subject Industrial Robot eng
dc.subject.ddc 620 | Ingenieurwissenschaften und Maschinenbau eng
dc.title Sensitivity-based Model Reduction for In-Process Identification of Industrial Robots Inverse Dynamics eng
dc.type conferenceObject eng
dc.type Text eng
dc.relation.doi 10.1109/ICMA49215.2020.9233709
dc.description.version acceptedVersion eng
tib.accessRights frei zug�nglich eng

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