Structure and Parameter Identification of Process Models with hard Non-linearities for Industrial Drive Trains by means of Degenerate Genetic Programming

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dc.identifier.uri http://dx.doi.org/10.15488/10398
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/10472
dc.contributor.author Tantau, Mathias eng
dc.contributor.author Perner, Lars eng
dc.contributor.author Wielitzka, Mark eng
dc.contributor.author Ortmaier, Tobias eng
dc.date.accessioned 2021-02-16T06:37:00Z
dc.date.available 2021-02-16T06:37:00Z
dc.date.issued 2019-07-29
dc.identifier.citation Tantau, M.; Perner, L.; Wielitzka, M.; Ortmaier, T.: Structure and Parameter Identification of Process Models with hard Non-linearities for Industrial Drive Trains by means of Degenerate Genetic Programming. In: Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics. Prague : SciTePress, 2019, S. 368-376. DOI: https://doi.org/10.5220/0007949003680376 eng
dc.description.abstract The derivation of bright-grey box models for electric drives with coupled mechanics, such as stacker cranes, robots and linear gantries is an important step in control design but often too time-consuming for the ordinary commissioning process. It requires structure and parameter identification in repeated trial and error loops. In this paper an automated genetic programming solution is proposed that can cope with various features, including highly non-linear mechanics (friction, backlash). The generated state space representation can readily be used for stability analysis, state control, Kalman filtering, etc. This, however, requires several special rules in the genetic programming procedure and an automated integration of features into the defining state space form. Simulations are carried out with industrial data to investigate the performance and robustness. eng
dc.language.iso eng eng
dc.publisher Prague : SciTePress
dc.relation.ispartof Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics eng
dc.rights CC BY-NC-ND 4.0 Unported eng
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ eng
dc.subject Genetic Programming eng
dc.subject Modelling eng
dc.subject Simultaneous Identification of Structure and Parameters eng
dc.subject Phenomenological Models eng
dc.subject Backlash eng
dc.subject Multiple-mass Resonators eng
dc.subject.ddc 620 | Ingenieurwissenschaften und Maschinenbau eng
dc.title Structure and Parameter Identification of Process Models with hard Non-linearities for Industrial Drive Trains by means of Degenerate Genetic Programming eng
dc.type bookPart eng
dc.type conferenceObject eng
dc.type Text
dc.relation.isbn 978-989-758-380-3
dc.relation.doi 10.5220/0007949003680376
dc.description.version publishedVersion eng
tib.accessRights frei zug�nglich eng


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