---
_id: '26389'
abstract:
- lang: eng
  text: Within this work, we investigate how data-driven numerical approximation methods
    of the Koopman operator can be used in practical control engineering applications.
    We refer to the method Extended Dynamic Mode Decomposition (EDMD), which approximates
    a nonlinear dynamical system as a linear model. This makes the method ideal for
    control engineering applications, because a linear system description is often
    assumed for this purpose. Using academic  examples, we simulatively analyze the
    prediction performance of the learned EDMD models and show how relevant system
    properties like stability, controllability, and observability are reflected by
    the EDMD model, which is a critical requirement for a successful control design
    process. Subsequently, we present our experimental results on a mechatronic test
    bench and evaluate the applicability to the control engineering design process.
    As a result, the investigated methods are suitable as a low-effort alternative
    for the design steps of model building and adaptation in the classical model-based
    controller design method.
author:
- first_name: Annika
  full_name: Junker, Annika
  id: '41470'
  last_name: Junker
  orcid: 0009-0002-6475-2503
- first_name: Julia
  full_name: Timmermann, Julia
  id: '15402'
  last_name: Timmermann
- first_name: Ansgar
  full_name: Trächtler, Ansgar
  id: '552'
  last_name: Trächtler
citation:
  ama: 'Junker A, Timmermann J, Trächtler A. Data-Driven Models for Control Engineering
    Applications Using the Koopman Operator. In: <i>2022 3rd International Conference
    on Artificial Intelligence, Robotics and Control (AIRC 2022)</i>. ; 2022:1-9.
    doi:<a href="https://doi.org/10.1109/AIRC56195.2022.9836980">10.1109/AIRC56195.2022.9836980</a>'
  apa: Junker, A., Timmermann, J., &#38; Trächtler, A. (2022). Data-Driven Models
    for Control Engineering Applications Using the Koopman Operator. <i>2022 3rd International
    Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)</i>, 1–9.
    <a href="https://doi.org/10.1109/AIRC56195.2022.9836980">https://doi.org/10.1109/AIRC56195.2022.9836980</a>
  bibtex: '@inproceedings{Junker_Timmermann_Trächtler_2022, title={Data-Driven Models
    for Control Engineering Applications Using the Koopman Operator}, DOI={<a href="https://doi.org/10.1109/AIRC56195.2022.9836980">10.1109/AIRC56195.2022.9836980</a>},
    booktitle={2022 3rd International Conference on Artificial Intelligence, Robotics
    and Control (AIRC 2022)}, author={Junker, Annika and Timmermann, Julia and Trächtler,
    Ansgar}, year={2022}, pages={1–9} }'
  chicago: Junker, Annika, Julia Timmermann, and Ansgar Trächtler. “Data-Driven Models
    for Control Engineering Applications Using the Koopman Operator.” In <i>2022 3rd
    International Conference on Artificial Intelligence, Robotics and Control (AIRC
    2022)</i>, 1–9, 2022. <a href="https://doi.org/10.1109/AIRC56195.2022.9836980">https://doi.org/10.1109/AIRC56195.2022.9836980</a>.
  ieee: 'A. Junker, J. Timmermann, and A. Trächtler, “Data-Driven Models for Control
    Engineering Applications Using the Koopman Operator,” in <i>2022 3rd International
    Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)</i>, Cairo,
    Egypt, 2022, pp. 1–9, doi: <a href="https://doi.org/10.1109/AIRC56195.2022.9836980">10.1109/AIRC56195.2022.9836980</a>.'
  mla: Junker, Annika, et al. “Data-Driven Models for Control Engineering Applications
    Using the Koopman Operator.” <i>2022 3rd International Conference on Artificial
    Intelligence, Robotics and Control (AIRC 2022)</i>, 2022, pp. 1–9, doi:<a href="https://doi.org/10.1109/AIRC56195.2022.9836980">10.1109/AIRC56195.2022.9836980</a>.
  short: 'A. Junker, J. Timmermann, A. Trächtler, in: 2022 3rd International Conference
    on Artificial Intelligence, Robotics and Control (AIRC 2022), 2022, pp. 1–9.'
conference:
  end_date: 2022-05-12
  location: Cairo, Egypt
  name: 2022 3rd International Conference on Artificial Intelligence, Robotics and
    Control (AIRC 2022)
  start_date: 2022-05-10
date_created: 2021-10-18T05:59:07Z
date_updated: 2026-04-01T05:51:06Z
department:
- _id: '153'
- _id: '880'
doi: 10.1109/AIRC56195.2022.9836980
keyword:
- Koopman Operator
- Nonlinear Control
- Extended Dynamic Mode Decomposition
- Hybrid Modelling
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9836980
page: 1-9
project:
- _id: '690'
  name: 'DART: Datengetriebene Methoden in der Regelungstechnik'
publication: 2022 3rd International Conference on Artificial Intelligence, Robotics
  and Control (AIRC 2022)
publication_identifier:
  isbn:
  - 978-1-6654-5946-4
publication_status: published
quality_controlled: '1'
status: public
title: Data-Driven Models for Control Engineering Applications Using the Koopman Operator
type: conference
user_id: '41470'
year: '2022'
...