---
_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: '61118'
abstract:
- lang: eng
text: Im Zuge der Digitalisierung erfahren maschinelles Lernen und datengetriebene
Methoden derzeit eine große Aufmerksamkeit in Wissenschaft und Industrie. Es fehlt
jedoch an Grundlagenwissen und Verständnis, wie die datengetriebenen Methoden
der Informatik mit bewährten modellbasierten Ingenieursmethoden wie dem modellbasierten
Entwurf in der Mechatronik und Methoden der Regelungstechnik sinnvoll kombiniert
werden können, um hybride Modelle zu erhalten. Diese ingenieurwissenschaftlichen
Methoden basieren auf physikalischen Verhaltensmodellen, die eine besonders verdichtete
und interpretierbare Darstellung von Wissen darstellen und insbesondere kausale
Zusammenhänge beschreiben. Für spezifische regelungstechnische Anwendungen gibt
es umfangreiches Vorwissen in Form von bekannten Strukturen und Informationen,
wie z.B. (Teil-)Modelle oder Parametersätze, die auch bei der Anwendung von Methoden
wie dem maschinellen Lernen genutzt werden sollten. Eine solche sinnvolle systematische
Verknüpfung ist wissenschaftlich, insbesondere im Hinblick auf die industrielle
Anwendung, noch nicht ausreichend untersucht worden und sehr vielversprechend.
In diesem Beitrag werden die Ergebnisse der Nachwuchsforschungsgruppe DART – Datengetriebene
Methoden in der Regelungstechnik vorgestellt. Das Hauptziel war es, die synergetische
Kombination von modell- und datengetriebenen Methoden für regelungstechnische
Aufgaben zu erforschen und es werden alle wichtigen Forschungsergebnisse aber
auch die verwendeten Grundprinzipien des maschinellen Lernens in diesem Beitrag
dargestellt.
- lang: eng
text: In the course of digitalization, machine learning and data-driven methods
are currently receiving a great deal of attention in science and industry. However,
there is a lack of basic knowledge and understanding of how data-driven methods
in computer science can be meaningfully combined with proven model-based engineering
methods such as model-based design in mechatronics and control engineering methods
to obtain hybrid models. These engineering methods are based on physical models,
which represent a particularly condensed and interpretable representation of knowledge
and, in particular, describe causal relationships. For specific control engineering
applications, there is extensive prior knowledge in the form of known structures
and information, such as (partial) models or parameter sets, which should also
be used when applying methods such as machine learning. Such a meaningful systematic
connection has not yet been sufficiently investigated scientifically, especially
with regard to industrial applications, and is very promising. This contribution
presents the results of the DART junior research group – Data-driven methods in
control engineering. The main objective was to investigate the synergistic combination
of model- and data-driven methods for control engineering tasks, and all important
research results as well as the basic principles of machine learning used are
presented in this publication.
author:
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Annika
full_name: Junker, Annika
id: '41470'
last_name: Junker
orcid: 0009-0002-6475-2503
- first_name: Michael
full_name: Hesse, Michael
id: '29222'
last_name: Hesse
- first_name: Luis
full_name: Schwarzer, Luis
last_name: Schwarzer
citation:
ama: Timmermann J, Götte R-S, Junker A, Hesse M, Schwarzer L. DART - Datengetriebene
Methoden in der Regelungstechnik. Vol Band 430. 1. Auflage. HNI Verlagsschriftenreihe;
2025. doi:10.17619/UNIPB/1-2305
apa: 'Timmermann, J., Götte, R.-S., Junker, A., Hesse, M., & Schwarzer, L. (2025).
DART - Datengetriebene Methoden in der Regelungstechnik: Vol. Band 430
(1. Auflage). HNI Verlagsschriftenreihe. https://doi.org/10.17619/UNIPB/1-2305'
bibtex: '@book{Timmermann_Götte_Junker_Hesse_Schwarzer_2025, place={Paderborn},
edition={1. Auflage}, title={DART - Datengetriebene Methoden in der Regelungstechnik},
volume={Band 430}, DOI={10.17619/UNIPB/1-2305},
publisher={HNI Verlagsschriftenreihe}, author={Timmermann, Julia and Götte, Ricarda-Samantha
and Junker, Annika and Hesse, Michael and Schwarzer, Luis}, year={2025} }'
chicago: 'Timmermann, Julia, Ricarda-Samantha Götte, Annika Junker, Michael Hesse,
and Luis Schwarzer. DART - Datengetriebene Methoden in der Regelungstechnik.
1. Auflage. Vol. Band 430. Paderborn: HNI Verlagsschriftenreihe, 2025. https://doi.org/10.17619/UNIPB/1-2305.'
ieee: 'J. Timmermann, R.-S. Götte, A. Junker, M. Hesse, and L. Schwarzer, DART
- Datengetriebene Methoden in der Regelungstechnik, 1. Auflage., vol. Band
430. Paderborn: HNI Verlagsschriftenreihe, 2025.'
mla: Timmermann, Julia, et al. DART - Datengetriebene Methoden in der Regelungstechnik.
1. Auflage, vol. Band 430, HNI Verlagsschriftenreihe, 2025, doi:10.17619/UNIPB/1-2305.
short: J. Timmermann, R.-S. Götte, A. Junker, M. Hesse, L. Schwarzer, DART - Datengetriebene
Methoden in der Regelungstechnik, 1. Auflage, HNI Verlagsschriftenreihe, Paderborn,
2025.
date_created: 2025-09-03T09:35:35Z
date_updated: 2026-04-01T06:14:00Z
department:
- _id: '880'
- _id: '153'
doi: 10.17619/UNIPB/1-2305
edition: 1. Auflage
language:
- iso: ger
main_file_link:
- open_access: '1'
url: https://digital.ub.uni-paderborn.de/doi/10.17619/UNIPB/1-2305
oa: '1'
place: Paderborn
publication_status: published
publisher: HNI Verlagsschriftenreihe
status: public
title: DART - Datengetriebene Methoden in der Regelungstechnik
type: book
user_id: '41470'
volume: Band 430
year: '2025'
...
---
_id: '56940'
abstract:
- lang: ger
text: "Ziel dieser Arbeit ist die Entwicklung eines modellbasierten Beobachters
für eingangsaffine, nichtlineare Systeme, der trotz Modellungenauigkeiten eine
hohe Schätzgüte erzielt und zusätzlich eine parametrische, physikalisch interpretierbare
Darstellung dieser ermöglicht. Diese soll zur automatisierten Verbesserung des
Modells verwendet werden. Die vorliegende Arbeit analysiert sowohl Techniken der
hybriden Systemidentifikation wie physikalisch motivierte neuronale Netze, als
auch Methoden zur Kompensation von Modellungenauigkeiten im Beobachterentwurf.
Basierend auf der Analyse wird ein neuartiger, modellbasierter Beobachter entworfen,
der Systemzustände und Modellungenauigkeiten gleichzeitig schätzt und insbesondere
eine parametrische, physikalisch interpretierbare Darstellung der Ungenauigkeiten
erzielt. Diese besteht aus einer Linearkombination von physikalisch interpretierbaren
Funktionen, deren dazugehörige, dünnbesetzt modellierte Parameter mithilfe eines
augmentierten Zustands parallel zu den Systemzuständen geschätzt werden. Das Novum
dieser Arbeit stellt somit die echtzeitfähige Schätzung von Zuständen und Modellungenauigkeiten
in physikalisch-technischer Form dar, auf deren Grundlage ein Konzept zur automatisierten
Modelladaption umgesetzt wird. Die Applikation der neuartigen Methode ist in der
Situation auftretender Systemveränderungen besonders vorteilhaft, da diese zur
Laufzeit durch den augmentierten Beobachter\r\ngeschätzt und identifiziert werden
können. "
- lang: eng
text: "The aim of this thesis is the development of a model-based observer for input-affine,
nonlinear systems that achieves a high estimation quality despite model inaccuracies.
By additionally providing a parametric, physically interpretable representation
of the model inaccuracies, an automated improvement of the model should be enabled.
This thesis\r\nanalyzes techniques of hybrid system identification such as physics-guided
neural networks, as well as methods for compensating model inaccuracies within
the observer design. Based on this analysis, a novel model-based observer is designed,
which estimates states and model inaccuracies jointly and, in particular, obtains
a parametric, physically\r\ninterpretable representation of the inaccuracies.
This consists of a linear combination of physically interpretable functions, whose
associated parameters are modeled sparse and estimated in parallel to the system’s
states using an augmented state. The novelty of this thesis is thus the real-time
capability to jointly estimate states and model inaccuracies in a physical-technical
manner, on the basis of which an automated model adaption can be\r\ncarried out.
The application of the new methodology is particularly advantageous in the situation
of occurring system changes since these can be estimated and identified at run
time by the augmented observer."
author:
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
citation:
ama: Götte R-S. Online-Schätzung von Modellungenauigkeiten zur automatischen
Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit.
Vol 423.; 2024. doi:10.17619/UNIPB/1-2066
apa: Götte, R.-S. (2024). Online-Schätzung von Modellungenauigkeiten zur automatischen
Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit
(Vol. 423). https://doi.org/10.17619/UNIPB/1-2066
bibtex: '@book{Götte_2024, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts},
title={Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption
unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit}, volume={423},
DOI={10.17619/UNIPB/1-2066},
author={Götte, Ricarda-Samantha}, year={2024}, collection={Verlagsschriftenreihe
des Heinz Nixdorf Instituts} }'
chicago: Götte, Ricarda-Samantha. Online-Schätzung von Modellungenauigkeiten
zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen
Interpretierbarkeit. Vol. 423. Verlagsschriftenreihe des Heinz Nixdorf Instituts,
2024. https://doi.org/10.17619/UNIPB/1-2066.
ieee: R.-S. Götte, Online-Schätzung von Modellungenauigkeiten zur automatischen
Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit,
vol. 423. 2024.
mla: Götte, Ricarda-Samantha. Online-Schätzung von Modellungenauigkeiten zur
automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen
Interpretierbarkeit. 2024, doi:10.17619/UNIPB/1-2066.
short: R.-S. Götte, Online-Schätzung von Modellungenauigkeiten zur automatischen
Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit,
2024.
date_created: 2024-11-07T11:43:05Z
date_updated: 2024-11-07T11:47:59Z
department:
- _id: '880'
- _id: '153'
doi: 10.17619/UNIPB/1-2066
intvolume: ' 423'
keyword:
- state estimation
- joint estimation
- sparsity
language:
- iso: ger
publication_identifier:
isbn:
- 978-3-947647-42-2
publication_status: published
series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts
status: public
supervisor:
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
- first_name: Ralf
full_name: Mikut, Ralf
last_name: Mikut
title: Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption
unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit
type: dissertation
user_id: '43992'
volume: 423
year: '2024'
...
---
_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: '58448'
abstract:
- lang: eng
text: Die Inbetriebnahme von Steuerungen und Regelungen stellt sicher, dass ein
mechatronisches System ordnungsgemäß funktioniert und den Anforderungen gerecht
wird. Der modellbasierte Entwurf basiert auf einem genauen Simulationsmodell.
Allerdings ist dieser klassische Weg bei komplexen Systemen oft nicht praktikabel,
da die analytische Modellierung zu kompliziert und zeitaufwendig ist. Diese Forschungslücke
wird durch Verfahren adressiert, die eine effiziente und sichere Inbetriebnahme
ermöglichen. Diese Verfahren kombinieren Regelungstechnik und Reinforcement Learning
und nutzen vorhandenes Wissen über die Regelungsaufgabe, um Korrekturen basierend
auf Messdaten und der probabilistischen Gauß-Prozess-Regression vorzunehmen. Das
Vorwissen kann als teilweise bekanntes physikalisches Modell oder als Steuerungsfunktion
vorliegen. Anwendungsbeispiele sind der Ultraschalldrahtbondprozess, verschiedene
Pendelsysteme und ein Hexapod. Eine angepasste Bayessche Optimierung wird zur
Identifikation einer Steuerparametrisierung für das Ultraschallbonden eingesetzt.
Außerdem wird eine hybride Optimalsteuerung für das Doppelpendel auf einem Wagen
entwickelt und erfolgreich validiert. Fur einen Hexapod zur Fahrzeugachsprüfung
wird eine hybride Zustandslinearisierung formuliert und ein Funktionsnachweis
im Rahmen einer Simulation erbracht. Die Einhaltung technischer Rahmenbedingungen
und stabiles Systemverhalten werden durch probabilistische Pradiktionen gewährleistet.
In allen Anwendungsfällen wird eine Steigerung der Effizienz und Güte erzielt.
- lang: eng
text: The commissioning of control systems ensures that a mechatronic system functions
properly and meets the requirements. Model-based design is based on a precise
simulation model. However, this classic approach is often impractical for complex
systems, as analytical modeling is too complicated and time-consuming. This research
gap is addressed by methods that enable efficient and safe commissioning. These
methods combine control engineering and reinforcement learning and use existing
knowledge about the control task to make corrections based on measurement data
and probabilistic Gaussian process regression. The prior knowledge can be available
as a partially known physical model or as a control function. Application examples
include the ultrasonic wire bonding process, various pendulum systems and a hexapod.
An adapted Bayesian optimization is used to identify a control parameterization
for ultrasonic bonding. In addition, a hybrid optimal control for the double pendulum
on a cart is developed and successfully validated. A hybrid state linearization
is formulated for a hexapod for vehicle axle testing and a proof of concept is
provided in a simulation. Compliance with technical framework conditions and stable
system behavior are ensured by probabilistic predictions. An increase in efficiency
and quality is achieved in all use cases.
author:
- first_name: Michael
full_name: Hesse, Michael
id: '29222'
last_name: Hesse
citation:
ama: Hesse M. Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell
bekannte dynamische Systeme mittels Gauß-Prozess-Regression. Vol 426. Heinz
Nixdorf Institut; 2024. doi:10.17619/UNIPB/1-2135
apa: Hesse, M. (2024). Interaktive Inbetriebnahme von Steuerungen und Regelungen
für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression (Vol.
426). Heinz Nixdorf Institut. https://doi.org/10.17619/UNIPB/1-2135
bibtex: '@book{Hesse_2024, place={Paderborn}, series={Verlagsschriftenreihe des
Heinz Nixdorf Instituts}, title={Interaktive Inbetriebnahme von Steuerungen und
Regelungen für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression},
volume={426}, DOI={10.17619/UNIPB/1-2135},
publisher={Heinz Nixdorf Institut}, author={Hesse, Michael}, year={2024}, collection={Verlagsschriftenreihe
des Heinz Nixdorf Instituts} }'
chicago: 'Hesse, Michael. Interaktive Inbetriebnahme von Steuerungen und Regelungen
für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression.
Vol. 426. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Paderborn: Heinz
Nixdorf Institut, 2024. https://doi.org/10.17619/UNIPB/1-2135.'
ieee: 'M. Hesse, Interaktive Inbetriebnahme von Steuerungen und Regelungen für
partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression, vol.
426. Paderborn: Heinz Nixdorf Institut, 2024.'
mla: Hesse, Michael. Interaktive Inbetriebnahme von Steuerungen und Regelungen
für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression.
Heinz Nixdorf Institut, 2024, doi:10.17619/UNIPB/1-2135.
short: M. Hesse, Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell
bekannte dynamische Systeme mittels Gauß-Prozess-Regression, Heinz Nixdorf Institut,
Paderborn, 2024.
date_created: 2025-01-30T14:45:46Z
date_updated: 2025-01-30T14:58:46Z
department:
- _id: '153'
- _id: '880'
doi: 10.17619/UNIPB/1-2135
intvolume: ' 426'
language:
- iso: ger
main_file_link:
- open_access: '1'
url: https://digital.ub.uni-paderborn.de/doi/10.17619/UNIPB/1-2135
oa: '1'
place: Paderborn
publication_identifier:
eissn:
- 2365-4422
isbn:
- 978-3-947647-45-3
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: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
title: Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell bekannte
dynamische Systeme mittels Gauß-Prozess-Regression
type: dissertation
user_id: '82875'
volume: 426
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: '34171'
abstract:
- lang: eng
text: State estimation when only a partial model of a considered system is available
remains a major challenge in many engineering fields. This work proposes a joint,
square-root unscented Kalman filter to estimate states and model uncertainties
simultaneously by linear combinations of physics-motivated library functions.
Using a sparsity promoting approach, a selection of those linear combinations
is chosen and thus an interpretable model can be extracted. Results indicate a
small estimation error compared to a traditional square-root unscented Kalman
filter and exhibit the enhancement of physically meaningful models.
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. Estimating States and Model Uncertainties Jointly
by a Sparsity Promoting UKF. In: 12th IFAC Symposium on Nonlinear Control Systems
(NOLCOS 2022). Vol 56. ; 2023:85-90. doi:https://doi.org/10.1016/j.ifacol.2023.02.015'
apa: Götte, R.-S., & Timmermann, J. (2023). Estimating States and Model Uncertainties
Jointly by a Sparsity Promoting UKF. 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), 56(1), 85–90. https://doi.org/10.1016/j.ifacol.2023.02.015
bibtex: '@inproceedings{Götte_Timmermann_2023, title={Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF}, volume={56}, DOI={https://doi.org/10.1016/j.ifacol.2023.02.015},
number={1}, booktitle={12th IFAC Symposium on Nonlinear Control Systems (NOLCOS
2022)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2023}, pages={85–90}
}'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF.” In 12th IFAC Symposium
on Nonlinear Control Systems (NOLCOS 2022), 56:85–90, 2023. https://doi.org/10.1016/j.ifacol.2023.02.015.
ieee: 'R.-S. Götte and J. Timmermann, “Estimating States and Model Uncertainties
Jointly by a Sparsity Promoting UKF,” in 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), Canberra, Australien, 2023, vol. 56, no. 1, pp. 85–90,
doi: https://doi.org/10.1016/j.ifacol.2023.02.015.'
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF.” 12th IFAC Symposium on
Nonlinear Control Systems (NOLCOS 2022), vol. 56, no. 1, 2023, pp. 85–90,
doi:https://doi.org/10.1016/j.ifacol.2023.02.015.
short: 'R.-S. Götte, J. Timmermann, in: 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), 2023, pp. 85–90.'
conference:
end_date: 2023-01-06
location: Canberra, Australien
name: 12th IFAC Symposium on Nonlinear Control Systems NOLCOS 2022
start_date: 2023-01-04
date_created: 2022-12-01T07:17:00Z
date_updated: 2024-11-13T08:43:05Z
department:
- _id: '153'
- _id: '880'
doi: https://doi.org/10.1016/j.ifacol.2023.02.015
intvolume: ' 56'
issue: '1'
keyword:
- joint estimation
- unscented transform
- Kalman filter
- sparsity
- data-driven
- compressed sensing
language:
- iso: eng
page: 85-90
publication: 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)
quality_controlled: '1'
status: public
title: Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF
type: conference
user_id: '43992'
volume: 56
year: '2023'
...
---
_id: '44326'
abstract:
- lang: eng
text: "Low-quality models that miss relevant dynamics lead to major challenges in
modelbased\r\nstate estimation. We address this issue by simultaneously estimating
the system’s states\r\nand its model inaccuracies by a square root unscented Kalman
filter (SRUKF). Concretely,\r\nwe augment the state with the parameter vector
of a linear combination containing suitable\r\nfunctions that approximate the
lacking dynamics. Presuming that only a few dynamical terms\r\nare relevant, the
parameter vector is claimed to be sparse. In Bayesian setting, properties like\r\nsparsity
are expressed by a prior distribution. One common choice for sparsity is a Laplace\r\ndistribution.
However, due to disadvantages of a Laplacian prior in regards to the SRUKF,\r\nthe
regularized horseshoe distribution, a Gaussian that approximately features sparsity,
is\r\napplied instead. Results exhibit small estimation errors with model improvements
detected by\r\nan automated model reduction technique."
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. Approximating a Laplacian Prior for Joint State and
Model Estimation within an UKF. In: IFAC-PapersOnLine. Vol 56. ; 2023:869-874.'
apa: Götte, R.-S., & Timmermann, J. (2023). Approximating a Laplacian Prior
for Joint State and Model Estimation within an UKF. IFAC-PapersOnLine,
56(2), 869–874.
bibtex: '@inproceedings{Götte_Timmermann_2023, title={Approximating a Laplacian
Prior for Joint State and Model Estimation within an UKF}, volume={56}, number={2},
booktitle={IFAC-PapersOnLine}, author={Götte, Ricarda-Samantha and Timmermann,
Julia}, year={2023}, pages={869–874} }'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian
Prior for Joint State and Model Estimation within an UKF.” In IFAC-PapersOnLine,
56:869–74, 2023.
ieee: R.-S. Götte and J. Timmermann, “Approximating a Laplacian Prior for Joint
State and Model Estimation within an UKF,” in IFAC-PapersOnLine, Yokohama,
Japan, 2023, vol. 56, no. 2, pp. 869–874.
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior
for Joint State and Model Estimation within an UKF.” IFAC-PapersOnLine,
vol. 56, no. 2, 2023, pp. 869–74.
short: 'R.-S. Götte, J. Timmermann, in: IFAC-PapersOnLine, 2023, pp. 869–874.'
conference:
end_date: 2023-07-14
location: Yokohama, Japan
name: 22nd IFAC World Congress
start_date: 2023-07-09
date_created: 2023-05-02T15:16:43Z
date_updated: 2024-11-13T08:42:37Z
department:
- _id: '153'
- _id: '880'
intvolume: ' 56'
issue: '2'
keyword:
- joint estimation
- unscented Kalman filter
- sparsity
- Laplacian prior
- regularized horseshoe
- principal component analysis
language:
- iso: eng
page: 869-874
publication: IFAC-PapersOnLine
quality_controlled: '1'
status: public
title: Approximating a Laplacian Prior for Joint State and Model Estimation within
an UKF
type: conference
user_id: '43992'
volume: 56
year: '2023'
...
---
_id: '48482'
author:
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Jo Noel
full_name: Klusmann, Jo Noel
last_name: Klusmann
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: 'Götte R-S, Klusmann JN, Timmermann J. Data-driven identification of disturbances
using a sliding mode observer. In: Proceedings - 33. Workshop Computational
Intelligence: Berlin, 23.-24. November 2023. ; 2023:113-123. doi:10.5445/KSP/1000162754'
apa: 'Götte, R.-S., Klusmann, J. N., & Timmermann, J. (2023). Data-driven identification
of disturbances using a sliding mode observer. Proceedings - 33. Workshop Computational
Intelligence: Berlin, 23.-24. November 2023, 113–123. https://doi.org/10.5445/KSP/1000162754'
bibtex: '@inproceedings{Götte_Klusmann_Timmermann_2023, title={Data-driven identification
of disturbances using a sliding mode observer}, DOI={10.5445/KSP/1000162754},
booktitle={Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24.
November 2023}, author={Götte, Ricarda-Samantha and Klusmann, Jo Noel and Timmermann,
Julia}, year={2023}, pages={113–123} }'
chicago: 'Götte, Ricarda-Samantha, Jo Noel Klusmann, and Julia Timmermann. “Data-Driven
Identification of Disturbances Using a Sliding Mode Observer.” In Proceedings
- 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023,
113–23, 2023. https://doi.org/10.5445/KSP/1000162754.'
ieee: 'R.-S. Götte, J. N. Klusmann, and J. Timmermann, “Data-driven identification
of disturbances using a sliding mode observer,” in Proceedings - 33. Workshop
Computational Intelligence: Berlin, 23.-24. November 2023, Berlin, Germany,
2023, pp. 113–123, doi: 10.5445/KSP/1000162754.'
mla: 'Götte, Ricarda-Samantha, et al. “Data-Driven Identification of Disturbances
Using a Sliding Mode Observer.” Proceedings - 33. Workshop Computational Intelligence:
Berlin, 23.-24. November 2023, 2023, pp. 113–23, doi:10.5445/KSP/1000162754.'
short: 'R.-S. Götte, J.N. Klusmann, J. Timmermann, in: Proceedings - 33. Workshop
Computational Intelligence: Berlin, 23.-24. November 2023, 2023, pp. 113–123.'
conference:
end_date: 2023-11-24
location: Berlin, Germany
name: 33. Workshop Computational Intelligence
start_date: 2023-11-23
date_created: 2023-10-26T08:11:25Z
date_updated: 2024-11-13T08:42:53Z
department:
- _id: '153'
- _id: '880'
doi: 10.5445/KSP/1000162754
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ksp.kit.edu/site/books/e/10.5445/KSP/1000162754/
oa: '1'
page: 113-123
publication: 'Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24.
November 2023'
quality_controlled: '1'
status: public
title: Data-driven identification of disturbances using a sliding mode observer
type: conference
user_id: '43992'
year: '2023'
...
---
_id: '48476'
author:
- first_name: Michael
full_name: Hesse, Michael
id: '29222'
last_name: Hesse
- 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, Timmermann J, Trächtler A. Hybrid Optimal Control for Dynamical Systems
using Gaussian Process Regression and Unscented Transform*. In: 2023
European Control Conference (ECC). IEEE; 2023. doi:10.23919/ecc57647.2023.10178368'
apa: Hesse, M., Timmermann, J., & Trächtler, A. (2023). Hybrid Optimal Control
for Dynamical Systems using Gaussian Process Regression and Unscented Transform*.
2023 European Control Conference (ECC). https://doi.org/10.23919/ecc57647.2023.10178368
bibtex: '@inproceedings{Hesse_Timmermann_Trächtler_2023, title={Hybrid Optimal Control
for Dynamical Systems using Gaussian Process Regression and Unscented Transform*},
DOI={10.23919/ecc57647.2023.10178368},
booktitle={2023 European Control Conference (ECC)}, publisher={IEEE}, author={Hesse,
Michael and Timmermann, Julia and Trächtler, Ansgar}, year={2023} }'
chicago: Hesse, Michael, Julia Timmermann, and Ansgar Trächtler. “Hybrid Optimal
Control for Dynamical Systems Using Gaussian Process Regression and Unscented
Transform*.” In 2023 European Control Conference (ECC). IEEE,
2023. https://doi.org/10.23919/ecc57647.2023.10178368.
ieee: 'M. Hesse, J. Timmermann, and A. Trächtler, “Hybrid Optimal Control for Dynamical
Systems using Gaussian Process Regression and Unscented Transform*,”
2023, doi: 10.23919/ecc57647.2023.10178368.'
mla: Hesse, Michael, et al. “Hybrid Optimal Control for Dynamical Systems Using
Gaussian Process Regression and Unscented Transform*.” 2023 European
Control Conference (ECC), IEEE, 2023, doi:10.23919/ecc57647.2023.10178368.
short: 'M. Hesse, J. Timmermann, A. Trächtler, in: 2023 European Control Conference
(ECC), IEEE, 2023.'
date_created: 2023-10-25T13:56:34Z
date_updated: 2024-11-13T08:43:40Z
department:
- _id: '153'
- _id: '880'
doi: 10.23919/ecc57647.2023.10178368
language:
- iso: eng
publication: 2023 European Control Conference (ECC)
publication_status: published
publisher: IEEE
quality_controlled: '1'
status: public
title: Hybrid Optimal Control for Dynamical Systems using Gaussian Process Regression
and Unscented Transform*
type: conference
user_id: '82875'
year: '2023'
...
---
_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: '43433'
abstract:
- lang: ger
text: 'Ziel dieser Studie ist es den digitalen moodlegestützten asynchronen Sprachkurs
Fachspezifisches Chinesisch für das „Maschinenbau in China Programm“ (mb-cn) der
Fakultät für Maschinenbau der Universität Paderborn zu evaluieren, um Handlungsempfehlungen
für zukünftig ähnlich aufgebaute Projekte zu entwickeln. Dazu wurden im Sommersemester
2021 sechs leitfadengestützte Interviews geführt. Die Interviews wurden anschließend
mithilfe von deduktiv ermittelten Kategorien, die sich aus dem Technology Acceptance
Model 2 (TAM2) nach Venkatesh und Davis (2000) ergaben, nach Mayring (2015) analysiert,
um abschließend die Forschungsfrage zu beantworten: „Wie bewerten mb-cn Ingenieurstudierende
die wahrgenommene Nützlichkeit der digitalen Sprachlernangebote des Kurses Fachspezifisches
Chinesisch?“.'
alternative_title:
- Evaluation of a digital subject-specific chinese language course for engineering
students
article_type: original
author:
- first_name: Dennis
full_name: Hambach, Dennis
id: '32850'
last_name: Hambach
citation:
ama: Hambach D. Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses
für Studierende des Ingenieurwesens. die hochschullehre Interdisziplinäre
Zeitschrift für Hochschule und Lehre. 2022;(8):1-15. doi:10.3278/HSL2249W
apa: Hambach, D. (2022). Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses
für Studierende des Ingenieurwesens. die hochschullehre. Interdisziplinäre
Zeitschrift für Hochschule und Lehre, 8, 1–15. https://doi.org/10.3278/HSL2249W
bibtex: '@article{Hambach_2022, title={Evaluation eines digitalen Fachspezifischen
Chinesischsprachkurses für Studierende des Ingenieurwesens}, DOI={10.3278/HSL2249W},
number={8}, journal={ die hochschullehre. Interdisziplinäre Zeitschrift für Hochschule
und Lehre}, publisher={wbv Publikation}, author={Hambach, Dennis}, year={2022},
pages={1–15} }'
chicago: 'Hambach, Dennis. “Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses
für Studierende des Ingenieurwesens.” die hochschullehre. Interdisziplinäre
Zeitschrift für Hochschule und Lehre, no. 8 (2022): 1–15. https://doi.org/10.3278/HSL2249W.'
ieee: 'D. Hambach, “Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses
für Studierende des Ingenieurwesens,” die hochschullehre. Interdisziplinäre
Zeitschrift für Hochschule und Lehre, no. 8, pp. 1–15, 2022, doi: 10.3278/HSL2249W.'
mla: Hambach, Dennis. “Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses
für Studierende des Ingenieurwesens.” die hochschullehre. Interdisziplinäre
Zeitschrift für Hochschule und Lehre, no. 8, wbv Publikation, 2022, pp. 1–15,
doi:10.3278/HSL2249W.
short: D. Hambach, die hochschullehre. Interdisziplinäre Zeitschrift für Hochschule
und Lehre (2022) 1–15.
date_created: 2023-04-06T09:59:20Z
date_updated: 2023-04-27T08:59:48Z
ddc:
- '490'
department:
- _id: '669'
- _id: '146'
- _id: '398'
doi: 10.3278/HSL2249W
has_accepted_license: '1'
issue: '8'
keyword:
- Technology Acceptance Model
- Fachspezifische Chinesischsprachkurse
- digitale Lehre
- Moodle
- Evaluation
language:
- iso: ger
main_file_link:
- open_access: '1'
url: https://www.wbv.de/shop/Evaluation-eines-digitalen-Fachspezifischen-Chinesischsprachkurses-fuer-Studierende-des-Ingenieurwesens-HSL2249W
oa: '1'
page: 1-15
publication: ' die hochschullehre. Interdisziplinäre Zeitschrift für Hochschule und
Lehre'
publication_status: published
publisher: wbv Publikation
quality_controlled: '1'
status: public
title: Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses für Studierende
des Ingenieurwesens
type: journal_article
user_id: '32850'
year: '2022'
...
---
_id: '26539'
abstract:
- lang: eng
text: In control design most control strategies are model-based and require accurate
models to be applied successfully. Due to simplifications and the model-reality-gap
physics-derived models frequently exhibit deviations from real-world-systems.
Likewise, purely data-driven methods often do not generalise well enough and may
violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired
loss functions separately have shown promising results to conquer these drawbacks.
In this contribution we extend existing methods towards the identification of
non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN
and a physics-inspired loss term (-L) to successfully identify the system's dynamics,
while maintaining the consistency with physical laws. The proposed method is demonstrated
on two real-world nonlinear systems and outperforms existing techniques regarding
complexity and reliability.
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. Composed Physics- and Data-driven System Identification
for Non-autonomous Systems in Control Engineering. In: 2022 3rd International
Conference on Artificial Intelligence, Robotics and Control (AIRC). ; 2022:67-76.
doi:10.1109/AIRC56195.2022.9836982'
apa: Götte, R.-S., & Timmermann, J. (2022). Composed Physics- and Data-driven
System Identification for Non-autonomous Systems in Control Engineering. 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 67–76. https://doi.org/10.1109/AIRC56195.2022.9836982
bibtex: '@inproceedings{Götte_Timmermann_2022, title={Composed Physics- and Data-driven
System Identification for Non-autonomous Systems in Control Engineering}, DOI={10.1109/AIRC56195.2022.9836982},
booktitle={2022 3rd International Conference on Artificial Intelligence, Robotics
and Control (AIRC)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2022},
pages={67–76} }'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven
System Identification for Non-Autonomous Systems in Control Engineering.” In 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 67–76, 2022. https://doi.org/10.1109/AIRC56195.2022.9836982.
ieee: 'R.-S. Götte and J. Timmermann, “Composed Physics- and Data-driven System
Identification for Non-autonomous Systems in Control Engineering,” in 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), Cairo, Egypt, 2022, pp. 67–76, doi: 10.1109/AIRC56195.2022.9836982.'
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven
System Identification for Non-Autonomous Systems in Control Engineering.” 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 2022, pp. 67–76, doi:10.1109/AIRC56195.2022.9836982.
short: 'R.-S. Götte, J. Timmermann, in: 2022 3rd International Conference on Artificial
Intelligence, Robotics and Control (AIRC), 2022, pp. 67–76.'
conference:
end_date: 2021-12-10
location: Cairo, Egypt
name: 3rd International Conference on Artificial Intelligence, Robotics and Control
start_date: 2021-12-08
date_created: 2021-10-19T14:47:17Z
date_updated: 2024-11-13T08:43:28Z
department:
- _id: '153'
- _id: '880'
doi: 10.1109/AIRC56195.2022.9836982
keyword:
- data-driven
- physics-based
- physics-informed
- neural networks
- system identification
- hybrid modelling
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2112.08148
oa: '1'
page: 67-76
publication: 2022 3rd International Conference on Artificial Intelligence, Robotics
and Control (AIRC)
quality_controlled: '1'
status: public
title: Composed Physics- and Data-driven System Identification for Non-autonomous
Systems in Control Engineering
type: conference
user_id: '43992'
year: '2022'
...
---
_id: '31066'
abstract:
- lang: eng
text: 'While trade-offs between modeling effort and model accuracy remain a major
concern with system identification, resorting to data-driven methods often leads
to a complete disregard for physical plausibility. To address this issue, we propose
a physics-guided hybrid approach for modeling non-autonomous systems under control.
Starting from a traditional physics-based model, this is extended by a recurrent
neural network and trained using a sophisticated multi-objective strategy yielding
physically plausible models. While purely data-driven methods fail to produce
satisfying results, experiments conducted on real data reveal substantial accuracy
improvements by our approach compared to a physics-based model. '
author:
- first_name: Oliver
full_name: Schön, Oliver
last_name: Schön
- 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: 'Schön O, Götte R-S, Timmermann J. Multi-Objective Physics-Guided Recurrent
Neural Networks for Identifying Non-Autonomous Dynamical Systems. In: 14th
IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022). Vol 55.
; 2022:19-24. doi:https://doi.org/10.1016/j.ifacol.2022.07.282'
apa: Schön, O., Götte, R.-S., & Timmermann, J. (2022). Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. 14th
IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 55(12),
19–24. https://doi.org/10.1016/j.ifacol.2022.07.282
bibtex: '@inproceedings{Schön_Götte_Timmermann_2022, title={Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}, volume={55},
DOI={https://doi.org/10.1016/j.ifacol.2022.07.282},
number={12}, booktitle={14th IFAC Workshop on Adaptive and Learning Control Systems
(ALCOS 2022)}, author={Schön, Oliver and Götte, Ricarda-Samantha and Timmermann,
Julia}, year={2022}, pages={19–24} }'
chicago: Schön, Oliver, Ricarda-Samantha Götte, and Julia Timmermann. “Multi-Objective
Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical
Systems.” In 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS
2022), 55:19–24, 2022. https://doi.org/10.1016/j.ifacol.2022.07.282.
ieee: 'O. Schön, R.-S. Götte, and J. Timmermann, “Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems,” in
14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022),
Casablanca, Morocco, 2022, vol. 55, no. 12, pp. 19–24, doi: https://doi.org/10.1016/j.ifacol.2022.07.282.'
mla: Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks
for Identifying Non-Autonomous Dynamical Systems.” 14th IFAC Workshop on Adaptive
and Learning Control Systems (ALCOS 2022), vol. 55, no. 12, 2022, pp. 19–24,
doi:https://doi.org/10.1016/j.ifacol.2022.07.282.
short: 'O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive
and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.'
conference:
end_date: 2022-07-01
location: Casablanca, Morocco
name: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)
start_date: 2022-06-29
date_created: 2022-05-05T06:22:55Z
date_updated: 2024-11-13T08:43:16Z
department:
- _id: '153'
- _id: '880'
doi: https://doi.org/10.1016/j.ifacol.2022.07.282
intvolume: ' 55'
issue: '12'
keyword:
- neural networks
- physics-guided
- data-driven
- multi-objective optimization
- system identification
- machine learning
- dynamical systems
language:
- iso: eng
page: 19-24
publication: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)
quality_controlled: '1'
status: public
title: Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous
Dynamical Systems
type: conference
user_id: '43992'
volume: 55
year: '2022'
...
---
_id: '29803'
abstract:
- lang: eng
text: "Ultrasonic wire bonding is a solid-state joining process used to form electrical
interconnections in micro and\r\npower electronics and batteries. A high frequency
oscillation causes a metallurgical bond deformation in\r\nthe contact area. Due
to the numerous physical influencing factors, it is very difficult to accurately
capture\r\nthis process in a model. Therefore, our goal is to determine a suitable
feed-forward control strategy for the\r\nbonding process even without detailed
model knowledge. We propose the use of batch constrained Bayesian\r\noptimization
for the control design. Hence, Bayesian optimization is precisely adapted to the
application of\r\nbonding: the constraint is used to check one quality feature
of the process and the use of batches leads to\r\nmore efficient experiments.
Our approach is suitable to determine a feed-forward control for the bonding\r\nprocess
that provides very high quality bonds without using a physical model. We also
show that the quality\r\nof the Bayesian optimization based control outperforms
random search as well as manual search by a user.\r\nUsing a simple prior knowledge
model derived from data further improves the quality of the connection.\r\nThe
Bayesian optimization approach offers the possibility to perform a sensitivity
analysis of the control\r\nparameters, which allows to evaluate the influence
of each control parameter on the bond quality. In summary,\r\nBayesian optimization
applied to the bonding process provides an excellent opportunity to develop a
feedforward\r\ncontrol without full modeling of the underlying physical processes."
author:
- first_name: Michael
full_name: Hesse, Michael
id: '29222'
last_name: Hesse
- first_name: Matthias
full_name: Hunstig, Matthias
last_name: Hunstig
- 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, Hunstig M, Timmermann J, Trächtler A. Batch Constrained Bayesian
Optimization for UltrasonicWire Bonding Feed-forward Control Design. In: Proceedings
of the 11th International Conference on Pattern Recognition Applications and Methods
(ICPRAM). ; 2022:383-394.'
apa: Hesse, M., Hunstig, M., Timmermann, J., & Trächtler, A. (2022). Batch Constrained
Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design.
Proceedings of the 11th International Conference on Pattern Recognition Applications
and Methods (ICPRAM), 383–394.
bibtex: '@inproceedings{Hesse_Hunstig_Timmermann_Trächtler_2022, title={Batch Constrained
Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition
Applications and Methods (ICPRAM)}, author={Hesse, Michael and Hunstig, Matthias
and Timmermann, Julia and Trächtler, Ansgar}, year={2022}, pages={383–394} }'
chicago: Hesse, Michael, Matthias Hunstig, Julia Timmermann, and Ansgar Trächtler.
“Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-Forward
Control Design.” In Proceedings of the 11th International Conference on Pattern
Recognition Applications and Methods (ICPRAM), 383–94, 2022.
ieee: M. Hesse, M. Hunstig, J. Timmermann, and A. Trächtler, “Batch Constrained
Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design,”
in Proceedings of the 11th International Conference on Pattern Recognition
Applications and Methods (ICPRAM), Online, 2022, pp. 383–394.
mla: Hesse, Michael, et al. “Batch Constrained Bayesian Optimization for UltrasonicWire
Bonding Feed-Forward Control Design.” Proceedings of the 11th International
Conference on Pattern Recognition Applications and Methods (ICPRAM), 2022,
pp. 383–94.
short: 'M. Hesse, M. Hunstig, J. Timmermann, A. Trächtler, in: Proceedings of the
11th International Conference on Pattern Recognition Applications and Methods
(ICPRAM), 2022, pp. 383–394.'
conference:
end_date: 2022-02-05
location: Online
name: 11th International Conference on Pattern Recognition Applications and Methods
start_date: 2022-02-03
date_created: 2022-02-09T12:50:25Z
date_updated: 2024-11-13T08:44:17Z
department:
- _id: '153'
- _id: '880'
keyword:
- Bayesian optimization
- Wire bonding
- Feed-forward control
- model-free design
language:
- iso: eng
page: 383-394
publication: Proceedings of the 11th International Conference on Pattern Recognition
Applications and Methods (ICPRAM)
publication_identifier:
isbn:
- 978-989-758-549-4
quality_controlled: '1'
status: public
title: Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward
Control Design
type: conference
user_id: '82875'
year: '2022'
...
---
_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'
...