---
_id: '59740'
abstract:
- lang: eng
text: ABSTRACTIn this contribution, we propose
an innovative method for determining optimal control sequences for nonlinear systems
with partially unknown dynamics, which further expands our previous work. Within
the paradigm of model‐based design, the practicality and safety of commissioning
feedforward controls and feedback controllers have priority. Our approach leverages
probabilistic Gaussian processes to adjust for model inaccuracies from measured
system data. This differs from conventional approaches that involve complicated
analytical modeling and may entail a substantial time investment to acquire expertise
and may prove impractical. Consequently, we address the limitations inherent in
traditional design methodologies. Our research focuses on the formulation and
solution of the hybrid1 optimal control problem using probabilistic
state predictions and multiple shooting. This ensures adaptability, data efficiency,
and resilience against uncertainties in system dynamics. These attributes are
empirically substantiated through experimental validation on a chaotic and highly
sensitive dynamical system—a double pendulum on a cart. Our methodology unfolds
as an iterative learning process, systematically exploring diverse controls, accumulating
data within each iteration, and refining the control strategy until the desired
task is accomplished. The adoption of the two‐degree‐of‐freedom control structure
allows for the distinct consideration of the feedforward and the feedback control
signal. For the latter, we employ a time‐variant, linear quadratic regulator (LQR)
designed to stabilize the system around its target trajectory. Furthermore, we
integrate a probabilistic long‐term prediction through the unscented transform,
enabling systematic anticipation of safety‐critical violations. Detailed insights
into relevant implementation aspects are provided. To ascertain the real‐world
applicability, we present an exemplary application involving a double pendulum
on a cart. The objective is to bring the pendulum arms from the lower stable to
the upper unstable equilibrium by horizontally moving the cart and subsequently
stabilize them. In this scenario, we assume that the centrifugal forces, crucial
to the system dynamics, have not been accurately modeled and must be learned from
data. Solving the control task took only 5 iterations and 1 h of computation time,
which surpasses our previous work [2], where we used the purely data‐driven PILCO
framework and required 27 iterations and 57 h of computation time. The time of
interaction with the system decreased by and the computation time is lowered
by . It demonstrates significant practical applicability for commissioning control systems.
author:
- first_name: Michael
full_name: Hesse, Michael
id: '29222'
last_name: Hesse
- first_name: Luis
full_name: Schwarzer, Luis
last_name: Schwarzer
- 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: Hesse M, Schwarzer L, Timmermann J, Trächtler A. Robust and Efficient Hybrid
Optimal Control via Gaussian Process Regression and Multiple Shooting With Experimental
Validation on a Double Pendulum on a Cart. PAMM. 2025;25(2). doi:10.1002/pamm.70004
apa: Hesse, M., Schwarzer, L., Timmermann, J., & Trächtler, A. (2025). Robust
and Efficient Hybrid Optimal Control via Gaussian Process Regression and Multiple
Shooting With Experimental Validation on a Double Pendulum on a Cart. PAMM,
25(2). https://doi.org/10.1002/pamm.70004
bibtex: '@article{Hesse_Schwarzer_Timmermann_Trächtler_2025, title={Robust and Efficient
Hybrid Optimal Control via Gaussian Process Regression and Multiple Shooting With
Experimental Validation on a Double Pendulum on a Cart}, volume={25}, DOI={10.1002/pamm.70004}, number={2},
journal={PAMM}, publisher={Wiley}, author={Hesse, Michael and Schwarzer, Luis
and Timmermann, Julia and Trächtler, Ansgar}, year={2025} }'
chicago: Hesse, Michael, Luis Schwarzer, Julia Timmermann, and Ansgar Trächtler.
“Robust and Efficient Hybrid Optimal Control via Gaussian Process Regression and
Multiple Shooting With Experimental Validation on a Double Pendulum on a Cart.”
PAMM 25, no. 2 (2025). https://doi.org/10.1002/pamm.70004.
ieee: 'M. Hesse, L. Schwarzer, J. Timmermann, and A. Trächtler, “Robust and Efficient
Hybrid Optimal Control via Gaussian Process Regression and Multiple Shooting With
Experimental Validation on a Double Pendulum on a Cart,” PAMM, vol. 25,
no. 2, 2025, doi: 10.1002/pamm.70004.'
mla: Hesse, Michael, et al. “Robust and Efficient Hybrid Optimal Control via Gaussian
Process Regression and Multiple Shooting With Experimental Validation on a Double
Pendulum on a Cart.” PAMM, vol. 25, no. 2, Wiley, 2025, doi:10.1002/pamm.70004.
short: M. Hesse, L. Schwarzer, J. Timmermann, A. Trächtler, PAMM 25 (2025).
date_created: 2025-04-30T08:18:46Z
date_updated: 2025-09-03T10:35:24Z
department:
- _id: '880'
- _id: '153'
doi: 10.1002/pamm.70004
intvolume: ' 25'
issue: '2'
language:
- iso: eng
project:
- _id: '690'
name: 'DART: Datengetriebene Methoden in der Regelungstechnik'
publication: PAMM
publication_identifier:
issn:
- 1617-7061
- 1617-7061
publication_status: published
publisher: Wiley
status: public
title: Robust and Efficient Hybrid Optimal Control via Gaussian Process Regression
and Multiple Shooting With Experimental Validation on a Double Pendulum on a Cart
type: journal_article
user_id: '15402'
volume: 25
year: '2025'
...
---
_id: '58164'
abstract:
- lang: ger
text: Der modellbasierte Regelungsentwurf erfordert eine möglichst genaue Kenntnis
über das dynamische Verhalten des zugrunde liegenden physikalischen Systems. Durch
maschinelle Lernverfahren besteht das Potenzial den Modellierungsaufwand im Vergleich
zum klassischen Vorgehen zu reduzieren, indem physikalisches Vorwissen und an
Messdaten trainierte Modelle effektiv zusammengeführt werden. Diese Dissertation
entwickelt Methoden zur datengetriebenen Bestimmung von Modellen für den Regelungsentwurf
nichtlinearer mechatronischer Systeme. Dazu wird die regelungstechnische Anwendbarkeit
von Koopman-Operator-basierten Verfahren analysiert, die nichtlineare Dynamiken
durch lineare Modelle approximieren. Darüber hinaus wird ein neuartiges Verfahren
zur datengetriebenen Bestimmung von Port-Hamilton-Modellen entwickelt, die Energiezusammenhänge
plausibel abbilden und sich unmittelbar für einen passivitätsbasierten Regelungsentwurf
verwenden lassen. Zudem werden Ansätze zur automatischen Aktualisierung des im
Regelkreis verwendeten Streckenmodells bei Modellunsicherheiten oder auftretenden
Veränderungen der Systemdynamik vorgestellt. Experimentelle sowie simulative Untersuchungen
demonstrieren die herausragende Prädiktionsgenauigkeit der datengetriebenen Modelle
und die hohe Regelgüte. Die Ergebnisse dieser Dissertation leisten einen bedeutenden
Beitrag, weil die datengetriebenen Modelle eine aus regelungstechnischer Sicht
verwertbare Form aufweisen. Sie sind physikalisch interpretierbar und lassen sich
nahtlos in bestehende Analyse- und Entwurfsmethoden einbinden. Dies eröffnet neue
Perspektiven für zukünftige Anwendungen und Weiterentwicklungen.
- lang: eng
text: Model-based control design requires accurate insight into the dynamic behavior
of the underlying physical system. Machine learning methods have the potential
to reduce modeling efforts compared to the classic approach by effectively combining
physical prior knowledge and models trained on measurement data. This dissertation
develops methods to determine data-driven models for the control design of nonlinear
mechatronic systems. For this purpose, the control applicability of Koopman operator-based
methods, which approximate nonlinear dynamics by linear models, is analyzed. In
addition, a novel method is developed for the data-driven determination of port-Hamiltonian
models, which plausibly represent energy flows and can be directly utilized for
passivity-based control design. Moreover, approaches for automatically updating
the plant model used in the control loop are presented in case of model uncertainties
or occuring changes in system dynamics during operation. Experimental and simulative
studies demonstrate the outstanding prediction accuracy of the data-driven models
and the high control performance. The findings of this dissertation make a significant
contribution because the data-driven models exhibit a form that is highly usable
for control engineering. They are physically interpretable and can be seamlessly
integrated into existing analysis and design methods. This opens new perspectives
for future applications and further developments.
author:
- first_name: Annika
full_name: Junker, Annika
id: '41470'
last_name: Junker
orcid: 0009-0002-6475-2503
citation:
ama: Junker A. Datengetriebene Modellbildung für nichtlineare mechatronische
Systeme in regelungstechnisch verwertbarer Form. Vol Band 428. Heinz Nixdorf
Institut; 2024. doi:10.17619/UNIPB/1-2158
apa: 'Junker, A. (2024). Datengetriebene Modellbildung für nichtlineare mechatronische
Systeme in regelungstechnisch verwertbarer Form: Vol. Band 428. Heinz Nixdorf
Institut. https://doi.org/10.17619/UNIPB/1-2158'
bibtex: '@book{Junker_2024, place={Paderborn}, series={Verlagsschriftenreihe des
Heinz Nixdorf Instituts}, title={Datengetriebene Modellbildung für nichtlineare
mechatronische Systeme in regelungstechnisch verwertbarer Form}, volume={Band
428}, DOI={10.17619/UNIPB/1-2158},
publisher={Heinz Nixdorf Institut}, author={Junker, Annika}, year={2024}, collection={Verlagsschriftenreihe
des Heinz Nixdorf Instituts} }'
chicago: 'Junker, Annika. Datengetriebene Modellbildung für nichtlineare mechatronische
Systeme in regelungstechnisch verwertbarer Form. Vol. Band 428. Verlagsschriftenreihe
des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, 2024. https://doi.org/10.17619/UNIPB/1-2158.'
ieee: 'A. Junker, Datengetriebene Modellbildung für nichtlineare mechatronische
Systeme in regelungstechnisch verwertbarer Form, vol. Band 428. Paderborn:
Heinz Nixdorf Institut, 2024.'
mla: Junker, Annika. Datengetriebene Modellbildung für nichtlineare mechatronische
Systeme in regelungstechnisch verwertbarer Form. Heinz Nixdorf Institut, 2024,
doi:10.17619/UNIPB/1-2158.
short: A. Junker, Datengetriebene Modellbildung für nichtlineare mechatronische
Systeme in regelungstechnisch verwertbarer Form, Heinz Nixdorf Institut, Paderborn,
2024.
date_created: 2025-01-13T11:19:30Z
date_updated: 2025-01-16T13:15:20Z
department:
- _id: '153'
- _id: '880'
doi: 10.17619/UNIPB/1-2158
language:
- iso: ger
main_file_link:
- open_access: '1'
url: https://digital.ub.uni-paderborn.de/hs/download/pdf/7770359
oa: '1'
place: Paderborn
project:
- _id: '690'
name: 'DART: Datengetriebene Methoden in der Regelungstechnik'
publication_identifier:
isbn:
- '9783947647477'
publication_status: published
publisher: Heinz Nixdorf Institut
series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts
status: public
supervisor:
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
- first_name: Boris
full_name: Lohmann, Boris
last_name: Lohmann
title: Datengetriebene Modellbildung für nichtlineare mechatronische Systeme in regelungstechnisch
verwertbarer Form
type: dissertation
user_id: '41470'
volume: Band 428
year: '2024'
...
---
_id: '57893'
abstract:
- lang: eng
text: AbstractControl engineering applications
usually require a model that accurately represents the dynamics of the system.
In addition to classical physical modeling, powerful data‐driven approaches are
gaining popularity. However, the resulting models may not be ideal for control
design due to their black‐box structure, which inherently limits interpretability.
Formulating the system dynamics in port‐Hamiltonian form is highly beneficial,
as its valuable property of passivity enables the straightforward design of globally
stable controllers while ensuring physical interpretability. In a recently published
article, we presented a method for data‐driven inference of port‐Hamiltonian models
for complex mechatronic systems, requiring only fundamental physical prior knowledge.
The resulting models accurately represent the nonlinear dynamics of the considered
systems and are physically interpretable. In this contribution, we advance our
previous work by including two key elements. Firstly, we demonstrate the application
of the above described data‐driven PCHD models for controller design. Preserving
the port‐Hamiltonian form in the closed loop not only guarantees global stability
and robustness but also ensures desired speed and damping characteristics. Since
control systems based on output measurements, which are continuously measured
during operation due to the feedback structure, we secondly aim to use this data.
Thus, we augment the existing modeling strategy with an intelligent adaptation
approach to address uncertainties and (un)predictable system changes in mechatronic
systems throughout their lifecycle, such as the installation of new components,
wear, or temperature fluctuations during operation. Our proposed algorithm for
recursively calculated data‐driven port‐Hamiltonian models utilizes a least‐squares
approach with extensions such as automatically adjusting the forgetting factor
and controlling the covariance matrix trace. We demonstrate the results through
model‐based application on an academic example and experimental validation on
a test bench.
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. Adaptive Data‐Driven Models in Port‐Hamiltonian
Form for Control Design. PAMM. 2024;25(1). doi:10.1002/pamm.202400154
apa: Junker, A., Timmermann, J., & Trächtler, A. (2024). Adaptive Data‐Driven
Models in Port‐Hamiltonian Form for Control Design. PAMM, 25(1).
https://doi.org/10.1002/pamm.202400154
bibtex: '@article{Junker_Timmermann_Trächtler_2024, title={Adaptive Data‐Driven
Models in Port‐Hamiltonian Form for Control Design}, volume={25}, DOI={10.1002/pamm.202400154},
number={1}, journal={PAMM}, publisher={Wiley}, author={Junker, Annika and Timmermann,
Julia and Trächtler, Ansgar}, year={2024} }'
chicago: Junker, Annika, Julia Timmermann, and Ansgar Trächtler. “Adaptive Data‐Driven
Models in Port‐Hamiltonian Form for Control Design.” PAMM 25, no. 1 (2024).
https://doi.org/10.1002/pamm.202400154.
ieee: 'A. Junker, J. Timmermann, and A. Trächtler, “Adaptive Data‐Driven Models
in Port‐Hamiltonian Form for Control Design,” PAMM, vol. 25, no. 1, 2024,
doi: 10.1002/pamm.202400154.'
mla: Junker, Annika, et al. “Adaptive Data‐Driven Models in Port‐Hamiltonian Form
for Control Design.” PAMM, vol. 25, no. 1, Wiley, 2024, doi:10.1002/pamm.202400154.
short: A. Junker, J. Timmermann, A. Trächtler, PAMM 25 (2024).
date_created: 2025-01-01T16:11:38Z
date_updated: 2025-09-03T09:33:23Z
department:
- _id: '153'
- _id: '880'
doi: 10.1002/pamm.202400154
intvolume: ' 25'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://onlinelibrary.wiley.com/doi/epdf/10.1002/pamm.202400154
oa: '1'
project:
- _id: '690'
name: 'DART: Datengetriebene Methoden in der Regelungstechnik'
publication: PAMM
publication_identifier:
issn:
- 1617-7061
- 1617-7061
publication_status: published
publisher: Wiley
quality_controlled: '1'
status: public
title: Adaptive Data‐Driven Models in Port‐Hamiltonian Form for Control Design
type: journal_article
user_id: '15402'
volume: 25
year: '2024'
...
---
_id: '59051'
abstract:
- lang: eng
text: 'Model‐based state observers require high‐quality models to deliver accurate
state estimates. However, due to time or cost shortage, modeling simplifications
or numerical issues, models often have severe inaccuracies that may lead to insufficient
and deficient control. Instead of attempting to iteratively model these deviations,
we address the challenge by the concept of joint estimation. Thus, we assume a
linear combination of suitable functions to approximate the inaccuracies. The
parameters of the linear combination are supposed to be time invariant and augment
the model''s state. Subsequently, the parameters can be identified simultaneously
to the states within the observer. Referring to the principle of Occam''s razor,
the parameters are claimed to be sparse. Our former work shows that estimating
states and model inaccuracies simultaneously by a sparsity promoting unscented
Kalman filter yields not only high accuracy but also provides interpretable representations
of underlying inaccuracies. Based on this work, our contribution is twofold: First,
we apply our approach finally on a real‐world test bench, namely a golf robot.
Within the experimental setting, we investigate closed loop behavior as well as
how suitable functions need to be chosen to approximate the inaccuracies in a
physically interpretable way. Results do not only provide high state estimation
accuracy but also meaningful insights into the system''s inaccuracies. Second,
we discuss and establish a method to automatically adapt and update the model
based on collected data of the linear combination during operation. Examining
past parameter estimates by principal component analysis, a moving window is utilized
to extract the most dominant functions. These are kept characterizing the model
inaccuracies, while nondominant functions are automatically neglected and refilled
with novel function candidates. After analysis and rebuilding, this updated function
set is subsequently fed back into the joint estimation loop and deployed for further
estimation. Hence, we give a holistic paradigm of how to analyze and combat model
inaccuracies while ensuring high state estimation accuracy. Within this setting,
we once more investigate closed loop behavior and yield promising results. In
conclusion, we show that the proposed observer provides a helpful tool to guarantee
high estimation accuracy for models with severe inaccuracies or for situations
with occurring deviations during operation, for example, due to mechanical wear
or temperature changes.'
author:
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: Götte R-S, Timmermann J. Online Learning With Joint State and Model Estimation.
PAMM. 2024;25(1). doi:10.1002/pamm.202400080
apa: Götte, R.-S., & Timmermann, J. (2024). Online Learning With Joint State
and Model Estimation. PAMM, 25(1). https://doi.org/10.1002/pamm.202400080
bibtex: '@article{Götte_Timmermann_2024, title={Online Learning With Joint State
and Model Estimation}, volume={25}, DOI={10.1002/pamm.202400080},
number={1}, journal={PAMM}, publisher={Wiley}, author={Götte, Ricarda-Samantha
and Timmermann, Julia}, year={2024} }'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Online Learning With Joint
State and Model Estimation.” PAMM 25, no. 1 (2024). https://doi.org/10.1002/pamm.202400080.
ieee: 'R.-S. Götte and J. Timmermann, “Online Learning With Joint State and Model
Estimation,” PAMM, vol. 25, no. 1, 2024, doi: 10.1002/pamm.202400080.'
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Online Learning With Joint
State and Model Estimation.” PAMM, vol. 25, no. 1, Wiley, 2024, doi:10.1002/pamm.202400080.
short: R.-S. Götte, J. Timmermann, PAMM 25 (2024).
date_created: 2025-03-17T07:06:12Z
date_updated: 2025-09-03T10:36:10Z
department:
- _id: '153'
- _id: '880'
doi: 10.1002/pamm.202400080
intvolume: ' 25'
issue: '1'
language:
- iso: eng
project:
- _id: '690'
name: 'DART: Datengetriebene Methoden in der Regelungstechnik'
publication: PAMM
publication_identifier:
issn:
- 1617-7061
- 1617-7061
publication_status: published
publisher: Wiley
status: public
title: Online Learning With Joint State and Model Estimation
type: journal_article
user_id: '15402'
volume: 25
year: '2024'
...
---
_id: '50070'
author:
- first_name: Annika
full_name: Junker, Annika
id: '41470'
last_name: Junker
orcid: 0009-0002-6475-2503
- first_name: Keno Egon Friedrich
full_name: Pape, Keno Egon Friedrich
id: '52024'
last_name: Pape
- 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, Pape KEF, Timmermann J, Trächtler A. Adaptive Koopman-Based Models
for Holistic Controller and Observer Design. IFAC-PapersOnLine. 2023;56(3):625-630.
doi:10.1016/j.ifacol.2023.12.094
apa: Junker, A., Pape, K. E. F., Timmermann, J., & Trächtler, A. (2023). Adaptive
Koopman-Based Models for Holistic Controller and Observer Design. IFAC-PapersOnLine,
56(3), 625–630. https://doi.org/10.1016/j.ifacol.2023.12.094
bibtex: '@article{Junker_Pape_Timmermann_Trächtler_2023, title={Adaptive Koopman-Based
Models for Holistic Controller and Observer Design}, volume={56}, DOI={10.1016/j.ifacol.2023.12.094},
number={3}, journal={IFAC-PapersOnLine}, publisher={Elsevier BV}, author={Junker,
Annika and Pape, Keno Egon Friedrich and Timmermann, Julia and Trächtler, Ansgar},
year={2023}, pages={625–630} }'
chicago: 'Junker, Annika, Keno Egon Friedrich Pape, Julia Timmermann, and Ansgar
Trächtler. “Adaptive Koopman-Based Models for Holistic Controller and Observer
Design.” IFAC-PapersOnLine 56, no. 3 (2023): 625–30. https://doi.org/10.1016/j.ifacol.2023.12.094.'
ieee: 'A. Junker, K. E. F. Pape, J. Timmermann, and A. Trächtler, “Adaptive Koopman-Based
Models for Holistic Controller and Observer Design,” IFAC-PapersOnLine,
vol. 56, no. 3, pp. 625–630, 2023, doi: 10.1016/j.ifacol.2023.12.094.'
mla: Junker, Annika, et al. “Adaptive Koopman-Based Models for Holistic Controller
and Observer Design.” IFAC-PapersOnLine, vol. 56, no. 3, Elsevier BV, 2023,
pp. 625–30, doi:10.1016/j.ifacol.2023.12.094.
short: A. Junker, K.E.F. Pape, J. Timmermann, A. Trächtler, IFAC-PapersOnLine 56
(2023) 625–630.
date_created: 2023-12-25T11:55:19Z
date_updated: 2024-11-13T12:28:18Z
department:
- _id: '153'
- _id: '880'
doi: 10.1016/j.ifacol.2023.12.094
intvolume: ' 56'
issue: '3'
keyword:
- General Medicine
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.ifacol.2023.12.094
oa: '1'
page: 625-630
project:
- _id: '690'
name: 'DART: Datengetriebene Methoden in der Regelungstechnik'
publication: IFAC-PapersOnLine
publication_identifier:
issn:
- 2405-8963
publication_status: published
publisher: Elsevier BV
quality_controlled: '1'
status: public
title: Adaptive Koopman-Based Models for Holistic Controller and Observer Design
type: journal_article
user_id: '41470'
volume: 56
year: '2023'
...
---
_id: '42238'
author:
- first_name: Annika
full_name: Junker, Annika
id: '41470'
last_name: Junker
orcid: 0009-0002-6475-2503
- first_name: Niklas
full_name: Fittkau, Niklas
id: '69890'
last_name: Fittkau
orcid: 0009-0007-1281-4465
- 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, Fittkau N, Timmermann J, Trächtler A. Autonomous Golf Putting with
Data-Driven and Physics-Based Methods. In: 2022 Sixth IEEE International Conference
on Robotic Computing (IRC). IEEE; 2023. doi:10.1109/irc55401.2022.00031'
apa: Junker, A., Fittkau, N., Timmermann, J., & Trächtler, A. (2023). Autonomous
Golf Putting with Data-Driven and Physics-Based Methods. 2022 Sixth IEEE International
Conference on Robotic Computing (IRC). 2022 Sixth IEEE International Conference
on Robotic Computing (IRC), Naples, Italy. https://doi.org/10.1109/irc55401.2022.00031
bibtex: '@inproceedings{Junker_Fittkau_Timmermann_Trächtler_2023, title={Autonomous
Golf Putting with Data-Driven and Physics-Based Methods}, DOI={10.1109/irc55401.2022.00031},
booktitle={2022 Sixth IEEE International Conference on Robotic Computing (IRC)},
publisher={IEEE}, author={Junker, Annika and Fittkau, Niklas and Timmermann, Julia
and Trächtler, Ansgar}, year={2023} }'
chicago: Junker, Annika, Niklas Fittkau, Julia Timmermann, and Ansgar Trächtler.
“Autonomous Golf Putting with Data-Driven and Physics-Based Methods.” In 2022
Sixth IEEE International Conference on Robotic Computing (IRC). IEEE, 2023.
https://doi.org/10.1109/irc55401.2022.00031.
ieee: 'A. Junker, N. Fittkau, J. Timmermann, and A. Trächtler, “Autonomous Golf
Putting with Data-Driven and Physics-Based Methods,” presented at the 2022 Sixth
IEEE International Conference on Robotic Computing (IRC), Naples, Italy, 2023,
doi: 10.1109/irc55401.2022.00031.'
mla: Junker, Annika, et al. “Autonomous Golf Putting with Data-Driven and Physics-Based
Methods.” 2022 Sixth IEEE International Conference on Robotic Computing (IRC),
IEEE, 2023, doi:10.1109/irc55401.2022.00031.
short: 'A. Junker, N. Fittkau, J. Timmermann, A. Trächtler, in: 2022 Sixth IEEE
International Conference on Robotic Computing (IRC), IEEE, 2023.'
conference:
end_date: 2022-12-07
location: Naples, Italy
name: 2022 Sixth IEEE International Conference on Robotic Computing (IRC)
start_date: 2022-12-05
date_created: 2023-02-20T08:10:39Z
date_updated: 2026-04-01T05:49:07Z
department:
- _id: '153'
- _id: '880'
doi: 10.1109/irc55401.2022.00031
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/10023639
project:
- _id: '690'
name: 'DART: Datengetriebene Methoden in der Regelungstechnik'
publication: 2022 Sixth IEEE International Conference on Robotic Computing (IRC)
publication_status: published
publisher: IEEE
quality_controlled: '1'
status: public
title: Autonomous Golf Putting with Data-Driven and Physics-Based Methods
type: conference
user_id: '41470'
year: '2023'
...
---
_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: 2022 3rd International Conference
on Artificial Intelligence, Robotics and Control (AIRC 2022). ; 2022:1-9.
doi:10.1109/AIRC56195.2022.9836980'
apa: Junker, A., Timmermann, J., & Trächtler, A. (2022). Data-Driven Models
for Control Engineering Applications Using the Koopman Operator. 2022 3rd International
Conference on Artificial Intelligence, Robotics and Control (AIRC 2022), 1–9.
https://doi.org/10.1109/AIRC56195.2022.9836980
bibtex: '@inproceedings{Junker_Timmermann_Trächtler_2022, title={Data-Driven Models
for Control Engineering Applications Using the Koopman Operator}, DOI={10.1109/AIRC56195.2022.9836980},
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 2022 3rd
International Conference on Artificial Intelligence, Robotics and Control (AIRC
2022), 1–9, 2022. https://doi.org/10.1109/AIRC56195.2022.9836980.
ieee: 'A. Junker, J. Timmermann, and A. Trächtler, “Data-Driven Models for Control
Engineering Applications Using the Koopman Operator,” in 2022 3rd International
Conference on Artificial Intelligence, Robotics and Control (AIRC 2022), Cairo,
Egypt, 2022, pp. 1–9, doi: 10.1109/AIRC56195.2022.9836980.'
mla: Junker, Annika, et al. “Data-Driven Models for Control Engineering Applications
Using the Koopman Operator.” 2022 3rd International Conference on Artificial
Intelligence, Robotics and Control (AIRC 2022), 2022, pp. 1–9, doi:10.1109/AIRC56195.2022.9836980.
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'
...
---
_id: '34011'
author:
- first_name: Annika
full_name: Junker, Annika
id: '41470'
last_name: Junker
orcid: 0009-0002-6475-2503
- first_name: Niklas
full_name: Fittkau, Niklas
id: '69890'
last_name: Fittkau
orcid: 0009-0007-1281-4465
- 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, Fittkau N, Timmermann J, Trächtler A. Autonomes Putten mittels datengetriebener
und physikbasierter Methoden. In: Proceedings - 32. Workshop Computational
Intelligence: Berlin, 1. - 2. Dezember 2022. ; 2022:119-124. doi:10.5445/KSP/1000151141'
apa: 'Junker, A., Fittkau, N., Timmermann, J., & Trächtler, A. (2022). Autonomes
Putten mittels datengetriebener und physikbasierter Methoden. Proceedings -
32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022, 119–124.
https://doi.org/10.5445/KSP/1000151141'
bibtex: '@inproceedings{Junker_Fittkau_Timmermann_Trächtler_2022, title={Autonomes
Putten mittels datengetriebener und physikbasierter Methoden}, DOI={10.5445/KSP/1000151141},
booktitle={Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. -
2. Dezember 2022}, author={Junker, Annika and Fittkau, Niklas and Timmermann,
Julia and Trächtler, Ansgar}, year={2022}, pages={119–124} }'
chicago: 'Junker, Annika, Niklas Fittkau, Julia Timmermann, and Ansgar Trächtler.
“Autonomes Putten Mittels Datengetriebener Und Physikbasierter Methoden.” In Proceedings
- 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022,
119–24, 2022. https://doi.org/10.5445/KSP/1000151141.'
ieee: 'A. Junker, N. Fittkau, J. Timmermann, and A. Trächtler, “Autonomes Putten
mittels datengetriebener und physikbasierter Methoden,” in Proceedings - 32.
Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022, Berlin,
Germany, 2022, pp. 119–124, doi: 10.5445/KSP/1000151141.'
mla: 'Junker, Annika, et al. “Autonomes Putten Mittels Datengetriebener Und Physikbasierter
Methoden.” Proceedings - 32. Workshop Computational Intelligence: Berlin, 1.
- 2. Dezember 2022, 2022, pp. 119–24, doi:10.5445/KSP/1000151141.'
short: 'A. Junker, N. Fittkau, J. Timmermann, A. Trächtler, in: Proceedings - 32.
Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022, 2022, pp.
119–124.'
conference:
end_date: 2022-12-02
location: Berlin, Germany
name: 32. Workshop Computational Intelligence
start_date: 2022-12-01
date_created: 2022-11-04T10:08:39Z
date_updated: 2026-04-01T05:59:13Z
department:
- _id: '153'
- _id: '880'
doi: 10.5445/KSP/1000151141
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://publikationen.bibliothek.kit.edu/1000151141
oa: '1'
page: 119-124
project:
- _id: '690'
name: 'DART: Datengetriebene Methoden in der Regelungstechnik'
publication: 'Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. -
2. Dezember 2022'
quality_controlled: '1'
status: public
title: Autonomes Putten mittels datengetriebener und physikbasierter Methoden
type: conference
user_id: '41470'
year: '2022'
...
---
_id: '50071'
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. Learning Data-Driven PCHD Models for Control
Engineering Applications*. IFAC-PapersOnLine. 2022;55(12):389-394. doi:10.1016/j.ifacol.2022.07.343
apa: Junker, A., Timmermann, J., & Trächtler, A. (2022). Learning Data-Driven
PCHD Models for Control Engineering Applications*. IFAC-PapersOnLine, 55(12),
389–394. https://doi.org/10.1016/j.ifacol.2022.07.343
bibtex: '@article{Junker_Timmermann_Trächtler_2022, title={Learning Data-Driven
PCHD Models for Control Engineering Applications*}, volume={55}, DOI={10.1016/j.ifacol.2022.07.343},
number={12}, journal={IFAC-PapersOnLine}, publisher={Elsevier BV}, author={Junker,
Annika and Timmermann, Julia and Trächtler, Ansgar}, year={2022}, pages={389–394}
}'
chicago: 'Junker, Annika, Julia Timmermann, and Ansgar Trächtler. “Learning Data-Driven
PCHD Models for Control Engineering Applications*.” IFAC-PapersOnLine 55,
no. 12 (2022): 389–94. https://doi.org/10.1016/j.ifacol.2022.07.343.'
ieee: 'A. Junker, J. Timmermann, and A. Trächtler, “Learning Data-Driven PCHD Models
for Control Engineering Applications*,” IFAC-PapersOnLine, vol. 55, no.
12, pp. 389–394, 2022, doi: 10.1016/j.ifacol.2022.07.343.'
mla: Junker, Annika, et al. “Learning Data-Driven PCHD Models for Control Engineering
Applications*.” IFAC-PapersOnLine, vol. 55, no. 12, Elsevier BV, 2022,
pp. 389–94, doi:10.1016/j.ifacol.2022.07.343.
short: A. Junker, J. Timmermann, A. Trächtler, IFAC-PapersOnLine 55 (2022) 389–394.
date_created: 2023-12-25T11:59:49Z
date_updated: 2026-04-01T06:15:18Z
department:
- _id: '153'
- _id: '880'
doi: 10.1016/j.ifacol.2022.07.343
intvolume: ' 55'
issue: '12'
keyword:
- Control and Systems Engineering
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.ifacol.2022.07.343
oa: '1'
page: 389-394
project:
- _id: '690'
name: 'DART: Datengetriebene Methoden in der Regelungstechnik'
publication: IFAC-PapersOnLine
publication_identifier:
issn:
- 2405-8963
publication_status: published
publisher: Elsevier BV
quality_controlled: '1'
status: public
title: Learning Data-Driven PCHD Models for Control Engineering Applications*
type: journal_article
user_id: '41470'
volume: 55
year: '2022'
...