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Authors: Mathias Tantau 1 ; Lars Perner 2 ; Mark Wielitzka 1 and Tobias Ortmaier 1

Affiliations: 1 Institute of Mechatronic Systems, Leibniz University Hanover, Appelstr. 11a, 30165 Hannover and Germany ; 2 Lenze Automation GmbH, Am Alten Bahnhof 11, D-38122 Braunschweig and Germany

ISBN: 978-989-758-380-3

Keyword(s): Genetic Programming, Modelling, Simultaneous Identification of Structure and Parameters, Phenomenological Models, Backlash, Multiple-mass Resonators.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computation and Control ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Optimization Algorithms ; Soft Computing

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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Paper citation in several formats:
Tantau, M.; Perner, L.; Wielitzka, M. and 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 Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 368-376. DOI: 10.5220/0007949003680376

@conference{icinco19,
author={Tantau, M. and Lars Perner. and Mark Wielitzka. and Tobias Ortmaier.},
title={Structure and Parameter Identification of Process Models with Hard Non-linearities for Industrial Drive Trains by Means of Degenerate Genetic Programming},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={368-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007949003680376},
isbn={978-989-758-380-3},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Structure and Parameter Identification of Process Models with Hard Non-linearities for Industrial Drive Trains by Means of Degenerate Genetic Programming
SN - 978-989-758-380-3
AU - Tantau, M.
AU - Perner, L.
AU - Wielitzka, M.
AU - Ortmaier, T.
PY - 2019
SP - 368
EP - 376
DO - 10.5220/0007949003680376

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