Structure and parameter identification of process models with hard non-linearities for industrial drive trains by means of degenerate genetic programming

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Autoren

  • Mathias Tantau
  • Lars Perner
  • Mark Wielitzka
  • Tobias Ortmaier

Organisationseinheiten

Externe Organisationen

  • Lenze SE
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Details

OriginalspracheEnglisch
Titel des SammelwerksProceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019)
Herausgeber/-innenOleg Gusikhin, Kurosh Madani, Janan Zaytoon
ErscheinungsortPrag
Seiten368-376
Seitenumfang9
ISBN (elektronisch)9789897583803
PublikationsstatusVeröffentlicht - 2019
Veranstaltung16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019 - Prague, Tschechische Republik
Dauer: 29 Juli 201931 Juli 2019

Publikationsreihe

NameICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics
Band1

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.

ASJC Scopus Sachgebiete

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Structure and parameter identification of process models with hard non-linearities for industrial drive trains by means of degenerate genetic programming. / Tantau, Mathias; Perner, Lars; Wielitzka, Mark et al.
Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019). Hrsg. / Oleg Gusikhin; Kurosh Madani; Janan Zaytoon. Prag, 2019. S. 368-376 (ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics; Band 1).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Tantau, M, Perner, L, Wielitzka, M & Ortmaier, T 2019, Structure and parameter identification of process models with hard non-linearities for industrial drive trains by means of degenerate genetic programming. in O Gusikhin, K Madani & J Zaytoon (Hrsg.), Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019). ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics, Bd. 1, Prag, S. 368-376, 16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019, Prague, Tschechische Republik, 29 Juli 2019. https://doi.org/10.5220/0007949003680376, https://doi.org/10.15488/10398
Tantau, M., Perner, L., Wielitzka, M., & Ortmaier, T. (2019). Structure and parameter identification of process models with hard non-linearities for industrial drive trains by means of degenerate genetic programming. In O. Gusikhin, K. Madani, & J. Zaytoon (Hrsg.), Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019) (S. 368-376). (ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics; Band 1).. https://doi.org/10.5220/0007949003680376, https://doi.org/10.15488/10398
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 Gusikhin O, Madani K, Zaytoon J, Hrsg., Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019). Prag. 2019. S. 368-376. (ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics). doi: 10.5220/0007949003680376, 10.15488/10398
Tantau, Mathias ; Perner, Lars ; Wielitzka, Mark et al. / Structure and parameter identification of process models with hard non-linearities for industrial drive trains by means of degenerate genetic programming. Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019). Hrsg. / Oleg Gusikhin ; Kurosh Madani ; Janan Zaytoon. Prag, 2019. S. 368-376 (ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics).
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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.",
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