[{"_id":"61118","user_id":"41470","department":[{"_id":"880"},{"_id":"153"}],"language":[{"iso":"ger"}],"type":"book","abstract":[{"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"},{"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."}],"status":"public","oa":"1","publisher":"HNI Verlagsschriftenreihe","date_updated":"2026-04-01T06:14:00Z","date_created":"2025-09-03T09:35:35Z","author":[{"first_name":"Julia","last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia"},{"first_name":"Ricarda-Samantha","last_name":"Götte","id":"43992","full_name":"Götte, Ricarda-Samantha"},{"id":"41470","full_name":"Junker, Annika","last_name":"Junker","orcid":"0009-0002-6475-2503","first_name":"Annika"},{"first_name":"Michael","last_name":"Hesse","full_name":"Hesse, Michael","id":"29222"},{"first_name":"Luis","last_name":"Schwarzer","full_name":"Schwarzer, Luis"}],"volume":"Band 430","title":"DART - Datengetriebene Methoden in der Regelungstechnik","main_file_link":[{"url":"https://digital.ub.uni-paderborn.de/doi/10.17619/UNIPB/1-2305","open_access":"1"}],"doi":"10.17619/UNIPB/1-2305","publication_status":"published","edition":"1. Auflage","place":"Paderborn","year":"2025","citation":{"ieee":"J. Timmermann, R.-S. Götte, A. Junker, M. Hesse, and L. Schwarzer, <i>DART - Datengetriebene Methoden in der Regelungstechnik</i>, 1. Auflage., vol. Band 430. Paderborn: HNI Verlagsschriftenreihe, 2025.","chicago":"Timmermann, Julia, Ricarda-Samantha Götte, Annika Junker, Michael Hesse, and Luis Schwarzer. <i>DART - Datengetriebene Methoden in der Regelungstechnik</i>. 1. Auflage. Vol. Band 430. Paderborn: HNI Verlagsschriftenreihe, 2025. <a href=\"https://doi.org/10.17619/UNIPB/1-2305\">https://doi.org/10.17619/UNIPB/1-2305</a>.","short":"J. Timmermann, R.-S. Götte, A. Junker, M. Hesse, L. Schwarzer, DART - Datengetriebene Methoden in der Regelungstechnik, 1. Auflage, HNI Verlagsschriftenreihe, Paderborn, 2025.","bibtex":"@book{Timmermann_Götte_Junker_Hesse_Schwarzer_2025, place={Paderborn}, edition={1. Auflage}, title={DART - Datengetriebene Methoden in der Regelungstechnik}, volume={Band 430}, DOI={<a href=\"https://doi.org/10.17619/UNIPB/1-2305\">10.17619/UNIPB/1-2305</a>}, publisher={HNI Verlagsschriftenreihe}, author={Timmermann, Julia and Götte, Ricarda-Samantha and Junker, Annika and Hesse, Michael and Schwarzer, Luis}, year={2025} }","mla":"Timmermann, Julia, et al. <i>DART - Datengetriebene Methoden in der Regelungstechnik</i>. 1. Auflage, vol. Band 430, HNI Verlagsschriftenreihe, 2025, doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2305\">10.17619/UNIPB/1-2305</a>.","ama":"Timmermann J, Götte R-S, Junker A, Hesse M, Schwarzer L. <i>DART - Datengetriebene Methoden in der Regelungstechnik</i>. Vol Band 430. 1. Auflage. HNI Verlagsschriftenreihe; 2025. doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2305\">10.17619/UNIPB/1-2305</a>","apa":"Timmermann, J., Götte, R.-S., Junker, A., Hesse, M., &#38; Schwarzer, L. (2025). <i>DART - Datengetriebene Methoden in der Regelungstechnik: Vol. Band 430</i> (1. Auflage). HNI Verlagsschriftenreihe. <a href=\"https://doi.org/10.17619/UNIPB/1-2305\">https://doi.org/10.17619/UNIPB/1-2305</a>"}},{"type":"dissertation","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."},{"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.","lang":"eng"}],"status":"public","_id":"58164","project":[{"_id":"690","name":"DART: Datengetriebene Methoden in der Regelungstechnik"}],"department":[{"_id":"153"},{"_id":"880"}],"series_title":"Verlagsschriftenreihe des Heinz Nixdorf Instituts","user_id":"41470","language":[{"iso":"ger"}],"publication_identifier":{"isbn":["9783947647477"]},"publication_status":"published","year":"2024","place":"Paderborn","citation":{"ama":"Junker A. <i>Datengetriebene Modellbildung für nichtlineare mechatronische Systeme in regelungstechnisch verwertbarer Form</i>. Vol Band 428. Heinz Nixdorf Institut; 2024. doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2158\">10.17619/UNIPB/1-2158</a>","ieee":"A. Junker, <i>Datengetriebene Modellbildung für nichtlineare mechatronische Systeme in regelungstechnisch verwertbarer Form</i>, vol. Band 428. Paderborn: Heinz Nixdorf Institut, 2024.","chicago":"Junker, Annika. <i>Datengetriebene Modellbildung für nichtlineare mechatronische Systeme in regelungstechnisch verwertbarer Form</i>. Vol. Band 428. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, 2024. <a href=\"https://doi.org/10.17619/UNIPB/1-2158\">https://doi.org/10.17619/UNIPB/1-2158</a>.","short":"A. Junker, Datengetriebene Modellbildung für nichtlineare mechatronische Systeme in regelungstechnisch verwertbarer Form, Heinz Nixdorf Institut, Paderborn, 2024.","mla":"Junker, Annika. <i>Datengetriebene Modellbildung für nichtlineare mechatronische Systeme in regelungstechnisch verwertbarer Form</i>. Heinz Nixdorf Institut, 2024, doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2158\">10.17619/UNIPB/1-2158</a>.","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={<a href=\"https://doi.org/10.17619/UNIPB/1-2158\">10.17619/UNIPB/1-2158</a>}, publisher={Heinz Nixdorf Institut}, author={Junker, Annika}, year={2024}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts} }","apa":"Junker, A. (2024). <i>Datengetriebene Modellbildung für nichtlineare mechatronische Systeme in regelungstechnisch verwertbarer Form: Vol. Band 428</i>. Heinz Nixdorf Institut. <a href=\"https://doi.org/10.17619/UNIPB/1-2158\">https://doi.org/10.17619/UNIPB/1-2158</a>"},"oa":"1","publisher":"Heinz Nixdorf Institut","date_updated":"2025-01-16T13:15:20Z","volume":"Band 428","date_created":"2025-01-13T11:19:30Z","author":[{"first_name":"Annika","orcid":"0009-0002-6475-2503","last_name":"Junker","id":"41470","full_name":"Junker, Annika"}],"supervisor":[{"first_name":"Julia","id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann"},{"first_name":"Boris","full_name":"Lohmann, Boris","last_name":"Lohmann"}],"title":"Datengetriebene Modellbildung für nichtlineare mechatronische Systeme in regelungstechnisch verwertbarer Form","doi":"10.17619/UNIPB/1-2158","main_file_link":[{"open_access":"1","url":"https://digital.ub.uni-paderborn.de/hs/download/pdf/7770359"}]},{"language":[{"iso":"eng"}],"department":[{"_id":"153"},{"_id":"880"}],"user_id":"15402","_id":"57893","project":[{"_id":"690","name":"DART: Datengetriebene Methoden in der Regelungstechnik"}],"status":"public","abstract":[{"text":"<jats:title>Abstract</jats:title><jats:p>Control 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.</jats:p>","lang":"eng"}],"publication":"PAMM","type":"journal_article","doi":"10.1002/pamm.202400154","main_file_link":[{"url":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pamm.202400154","open_access":"1"}],"title":"Adaptive Data‐Driven Models in Port‐Hamiltonian Form for Control Design","volume":25,"date_created":"2025-01-01T16:11:38Z","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","last_name":"Trächtler","id":"552","full_name":"Trächtler, Ansgar"}],"oa":"1","publisher":"Wiley","date_updated":"2025-09-03T09:33:23Z","intvolume":"        25","citation":{"mla":"Junker, Annika, et al. “Adaptive Data‐Driven Models in Port‐Hamiltonian Form for Control Design.” <i>PAMM</i>, vol. 25, no. 1, Wiley, 2024, doi:<a href=\"https://doi.org/10.1002/pamm.202400154\">10.1002/pamm.202400154</a>.","bibtex":"@article{Junker_Timmermann_Trächtler_2024, title={Adaptive Data‐Driven Models in Port‐Hamiltonian Form for Control Design}, volume={25}, DOI={<a href=\"https://doi.org/10.1002/pamm.202400154\">10.1002/pamm.202400154</a>}, number={1}, journal={PAMM}, publisher={Wiley}, author={Junker, Annika and Timmermann, Julia and Trächtler, Ansgar}, year={2024} }","short":"A. Junker, J. Timmermann, A. Trächtler, PAMM 25 (2024).","apa":"Junker, A., Timmermann, J., &#38; Trächtler, A. (2024). Adaptive Data‐Driven Models in Port‐Hamiltonian Form for Control Design. <i>PAMM</i>, <i>25</i>(1). <a href=\"https://doi.org/10.1002/pamm.202400154\">https://doi.org/10.1002/pamm.202400154</a>","ieee":"A. Junker, J. Timmermann, and A. Trächtler, “Adaptive Data‐Driven Models in Port‐Hamiltonian Form for Control Design,” <i>PAMM</i>, vol. 25, no. 1, 2024, doi: <a href=\"https://doi.org/10.1002/pamm.202400154\">10.1002/pamm.202400154</a>.","chicago":"Junker, Annika, Julia Timmermann, and Ansgar Trächtler. “Adaptive Data‐Driven Models in Port‐Hamiltonian Form for Control Design.” <i>PAMM</i> 25, no. 1 (2024). <a href=\"https://doi.org/10.1002/pamm.202400154\">https://doi.org/10.1002/pamm.202400154</a>.","ama":"Junker A, Timmermann J, Trächtler A. Adaptive Data‐Driven Models in Port‐Hamiltonian Form for Control Design. <i>PAMM</i>. 2024;25(1). doi:<a href=\"https://doi.org/10.1002/pamm.202400154\">10.1002/pamm.202400154</a>"},"year":"2024","issue":"1","quality_controlled":"1","publication_identifier":{"issn":["1617-7061","1617-7061"]},"publication_status":"published"},{"language":[{"iso":"eng"}],"keyword":["General Medicine"],"publication":"IFAC-PapersOnLine","date_created":"2023-12-25T11:55:19Z","publisher":"Elsevier BV","title":"Adaptive Koopman-Based Models for Holistic Controller and Observer Design","issue":"3","quality_controlled":"1","year":"2023","department":[{"_id":"153"},{"_id":"880"}],"user_id":"41470","_id":"50070","project":[{"_id":"690","name":"DART: Datengetriebene Methoden in der Regelungstechnik"}],"type":"journal_article","status":"public","volume":56,"author":[{"full_name":"Junker, Annika","id":"41470","orcid":"0009-0002-6475-2503","last_name":"Junker","first_name":"Annika"},{"first_name":"Keno Egon Friedrich","id":"52024","full_name":"Pape, Keno Egon Friedrich","last_name":"Pape"},{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"},{"full_name":"Trächtler, Ansgar","id":"552","last_name":"Trächtler","first_name":"Ansgar"}],"date_updated":"2024-11-13T12:28:18Z","oa":"1","doi":"10.1016/j.ifacol.2023.12.094","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.ifacol.2023.12.094"}],"publication_identifier":{"issn":["2405-8963"]},"publication_status":"published","page":"625-630","intvolume":"        56","citation":{"apa":"Junker, A., Pape, K. E. F., Timmermann, J., &#38; Trächtler, A. (2023). Adaptive Koopman-Based Models for Holistic Controller and Observer Design. <i>IFAC-PapersOnLine</i>, <i>56</i>(3), 625–630. <a href=\"https://doi.org/10.1016/j.ifacol.2023.12.094\">https://doi.org/10.1016/j.ifacol.2023.12.094</a>","bibtex":"@article{Junker_Pape_Timmermann_Trächtler_2023, title={Adaptive Koopman-Based Models for Holistic Controller and Observer Design}, volume={56}, DOI={<a href=\"https://doi.org/10.1016/j.ifacol.2023.12.094\">10.1016/j.ifacol.2023.12.094</a>}, 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} }","short":"A. Junker, K.E.F. Pape, J. Timmermann, A. Trächtler, IFAC-PapersOnLine 56 (2023) 625–630.","mla":"Junker, Annika, et al. “Adaptive Koopman-Based Models for Holistic Controller and Observer Design.” <i>IFAC-PapersOnLine</i>, vol. 56, no. 3, Elsevier BV, 2023, pp. 625–30, doi:<a href=\"https://doi.org/10.1016/j.ifacol.2023.12.094\">10.1016/j.ifacol.2023.12.094</a>.","ieee":"A. Junker, K. E. F. Pape, J. Timmermann, and A. Trächtler, “Adaptive Koopman-Based Models for Holistic Controller and Observer Design,” <i>IFAC-PapersOnLine</i>, vol. 56, no. 3, pp. 625–630, 2023, doi: <a href=\"https://doi.org/10.1016/j.ifacol.2023.12.094\">10.1016/j.ifacol.2023.12.094</a>.","chicago":"Junker, Annika, Keno Egon Friedrich Pape, Julia Timmermann, and Ansgar Trächtler. “Adaptive Koopman-Based Models for Holistic Controller and Observer Design.” <i>IFAC-PapersOnLine</i> 56, no. 3 (2023): 625–30. <a href=\"https://doi.org/10.1016/j.ifacol.2023.12.094\">https://doi.org/10.1016/j.ifacol.2023.12.094</a>.","ama":"Junker A, Pape KEF, Timmermann J, Trächtler A. Adaptive Koopman-Based Models for Holistic Controller and Observer Design. <i>IFAC-PapersOnLine</i>. 2023;56(3):625-630. doi:<a href=\"https://doi.org/10.1016/j.ifacol.2023.12.094\">10.1016/j.ifacol.2023.12.094</a>"}},{"status":"public","publication":"2022 Sixth IEEE International Conference on Robotic Computing (IRC)","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"153"},{"_id":"880"}],"user_id":"41470","_id":"42238","project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"citation":{"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: <a href=\"https://doi.org/10.1109/irc55401.2022.00031\">10.1109/irc55401.2022.00031</a>.","chicago":"Junker, Annika, Niklas Fittkau, Julia Timmermann, and Ansgar Trächtler. “Autonomous Golf Putting with Data-Driven and Physics-Based Methods.” In <i>2022 Sixth IEEE International Conference on Robotic Computing (IRC)</i>. IEEE, 2023. <a href=\"https://doi.org/10.1109/irc55401.2022.00031\">https://doi.org/10.1109/irc55401.2022.00031</a>.","ama":"Junker A, Fittkau N, Timmermann J, Trächtler A. Autonomous Golf Putting with Data-Driven and Physics-Based Methods. In: <i>2022 Sixth IEEE International Conference on Robotic Computing (IRC)</i>. IEEE; 2023. doi:<a href=\"https://doi.org/10.1109/irc55401.2022.00031\">10.1109/irc55401.2022.00031</a>","apa":"Junker, A., Fittkau, N., Timmermann, J., &#38; Trächtler, A. (2023). Autonomous Golf Putting with Data-Driven and Physics-Based Methods. <i>2022 Sixth IEEE International Conference on Robotic Computing (IRC)</i>. 2022 Sixth IEEE International Conference on Robotic Computing (IRC), Naples, Italy. <a href=\"https://doi.org/10.1109/irc55401.2022.00031\">https://doi.org/10.1109/irc55401.2022.00031</a>","short":"A. Junker, N. Fittkau, J. Timmermann, A. Trächtler, in: 2022 Sixth IEEE International Conference on Robotic Computing (IRC), IEEE, 2023.","bibtex":"@inproceedings{Junker_Fittkau_Timmermann_Trächtler_2023, title={Autonomous Golf Putting with Data-Driven and Physics-Based Methods}, DOI={<a href=\"https://doi.org/10.1109/irc55401.2022.00031\">10.1109/irc55401.2022.00031</a>}, 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} }","mla":"Junker, Annika, et al. “Autonomous Golf Putting with Data-Driven and Physics-Based Methods.” <i>2022 Sixth IEEE International Conference on Robotic Computing (IRC)</i>, IEEE, 2023, doi:<a href=\"https://doi.org/10.1109/irc55401.2022.00031\">10.1109/irc55401.2022.00031</a>."},"year":"2023","quality_controlled":"1","publication_status":"published","conference":{"end_date":"2022-12-07","location":"Naples, Italy","name":"2022 Sixth IEEE International Conference on Robotic Computing (IRC)","start_date":"2022-12-05"},"doi":"10.1109/irc55401.2022.00031","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/10023639"}],"title":"Autonomous Golf Putting with Data-Driven and Physics-Based Methods","date_created":"2023-02-20T08:10:39Z","author":[{"full_name":"Junker, Annika","id":"41470","last_name":"Junker","orcid":"0009-0002-6475-2503","first_name":"Annika"},{"id":"69890","full_name":"Fittkau, Niklas","orcid":"0009-0007-1281-4465","last_name":"Fittkau","first_name":"Niklas"},{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"},{"first_name":"Ansgar","id":"552","full_name":"Trächtler, Ansgar","last_name":"Trächtler"}],"publisher":"IEEE","date_updated":"2026-04-01T05:49:07Z"},{"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9836980"}],"conference":{"location":"Cairo, Egypt","end_date":"2022-05-12","start_date":"2022-05-10","name":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)"},"doi":"10.1109/AIRC56195.2022.9836980","title":"Data-Driven Models for Control Engineering Applications Using the Koopman Operator","author":[{"first_name":"Annika","id":"41470","full_name":"Junker, Annika","orcid":"0009-0002-6475-2503","last_name":"Junker"},{"first_name":"Julia","id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann"},{"id":"552","full_name":"Trächtler, Ansgar","last_name":"Trächtler","first_name":"Ansgar"}],"date_created":"2021-10-18T05:59:07Z","date_updated":"2026-04-01T05:51:06Z","citation":{"ieee":"A. Junker, J. Timmermann, and A. Trächtler, “Data-Driven Models for Control Engineering Applications Using the Koopman Operator,” in <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)</i>, Cairo, Egypt, 2022, pp. 1–9, doi: <a href=\"https://doi.org/10.1109/AIRC56195.2022.9836980\">10.1109/AIRC56195.2022.9836980</a>.","chicago":"Junker, Annika, Julia Timmermann, and Ansgar Trächtler. “Data-Driven Models for Control Engineering Applications Using the Koopman Operator.” In <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)</i>, 1–9, 2022. <a href=\"https://doi.org/10.1109/AIRC56195.2022.9836980\">https://doi.org/10.1109/AIRC56195.2022.9836980</a>.","ama":"Junker A, Timmermann J, Trächtler A. Data-Driven Models for Control Engineering Applications Using the Koopman Operator. In: <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)</i>. ; 2022:1-9. doi:<a href=\"https://doi.org/10.1109/AIRC56195.2022.9836980\">10.1109/AIRC56195.2022.9836980</a>","mla":"Junker, Annika, et al. “Data-Driven Models for Control Engineering Applications Using the Koopman Operator.” <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)</i>, 2022, pp. 1–9, doi:<a href=\"https://doi.org/10.1109/AIRC56195.2022.9836980\">10.1109/AIRC56195.2022.9836980</a>.","short":"A. Junker, J. Timmermann, A. Trächtler, in: 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022), 2022, pp. 1–9.","bibtex":"@inproceedings{Junker_Timmermann_Trächtler_2022, title={Data-Driven Models for Control Engineering Applications Using the Koopman Operator}, DOI={<a href=\"https://doi.org/10.1109/AIRC56195.2022.9836980\">10.1109/AIRC56195.2022.9836980</a>}, booktitle={2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)}, author={Junker, Annika and Timmermann, Julia and Trächtler, Ansgar}, year={2022}, pages={1–9} }","apa":"Junker, A., Timmermann, J., &#38; Trächtler, A. (2022). Data-Driven Models for Control Engineering Applications Using the Koopman Operator. <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)</i>, 1–9. <a href=\"https://doi.org/10.1109/AIRC56195.2022.9836980\">https://doi.org/10.1109/AIRC56195.2022.9836980</a>"},"page":"1-9","year":"2022","publication_status":"published","quality_controlled":"1","publication_identifier":{"isbn":["978-1-6654-5946-4"]},"language":[{"iso":"eng"}],"keyword":["Koopman Operator","Nonlinear Control","Extended Dynamic Mode Decomposition","Hybrid Modelling"],"user_id":"41470","department":[{"_id":"153"},{"_id":"880"}],"project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"_id":"26389","status":"public","abstract":[{"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.","lang":"eng"}],"type":"conference","publication":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)"},{"oa":"1","date_updated":"2026-04-01T05:59:13Z","date_created":"2022-11-04T10:08:39Z","author":[{"last_name":"Junker","orcid":"0009-0002-6475-2503","full_name":"Junker, Annika","id":"41470","first_name":"Annika"},{"first_name":"Niklas","last_name":"Fittkau","orcid":"0009-0007-1281-4465","full_name":"Fittkau, Niklas","id":"69890"},{"last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402","first_name":"Julia"},{"full_name":"Trächtler, Ansgar","id":"552","last_name":"Trächtler","first_name":"Ansgar"}],"title":"Autonomes Putten mittels datengetriebener und physikbasierter Methoden","conference":{"name":"32. Workshop Computational Intelligence","start_date":"2022-12-01","end_date":"2022-12-02","location":"Berlin, Germany"},"doi":"10.5445/KSP/1000151141","main_file_link":[{"open_access":"1","url":"https://publikationen.bibliothek.kit.edu/1000151141"}],"quality_controlled":"1","year":"2022","page":"119-124","citation":{"mla":"Junker, Annika, et al. “Autonomes Putten Mittels Datengetriebener Und Physikbasierter Methoden.” <i>Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022</i>, 2022, pp. 119–24, doi:<a href=\"https://doi.org/10.5445/KSP/1000151141\">10.5445/KSP/1000151141</a>.","bibtex":"@inproceedings{Junker_Fittkau_Timmermann_Trächtler_2022, title={Autonomes Putten mittels datengetriebener und physikbasierter Methoden}, DOI={<a href=\"https://doi.org/10.5445/KSP/1000151141\">10.5445/KSP/1000151141</a>}, 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} }","short":"A. Junker, N. Fittkau, J. Timmermann, A. Trächtler, in: Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022, 2022, pp. 119–124.","apa":"Junker, A., Fittkau, N., Timmermann, J., &#38; Trächtler, A. (2022). Autonomes Putten mittels datengetriebener und physikbasierter Methoden. <i>Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022</i>, 119–124. <a href=\"https://doi.org/10.5445/KSP/1000151141\">https://doi.org/10.5445/KSP/1000151141</a>","chicago":"Junker, Annika, Niklas Fittkau, Julia Timmermann, and Ansgar Trächtler. “Autonomes Putten Mittels Datengetriebener Und Physikbasierter Methoden.” In <i>Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022</i>, 119–24, 2022. <a href=\"https://doi.org/10.5445/KSP/1000151141\">https://doi.org/10.5445/KSP/1000151141</a>.","ieee":"A. Junker, N. Fittkau, J. Timmermann, and A. Trächtler, “Autonomes Putten mittels datengetriebener und physikbasierter Methoden,” in <i>Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022</i>, Berlin, Germany, 2022, pp. 119–124, doi: <a href=\"https://doi.org/10.5445/KSP/1000151141\">10.5445/KSP/1000151141</a>.","ama":"Junker A, Fittkau N, Timmermann J, Trächtler A. Autonomes Putten mittels datengetriebener und physikbasierter Methoden. In: <i>Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022</i>. ; 2022:119-124. doi:<a href=\"https://doi.org/10.5445/KSP/1000151141\">10.5445/KSP/1000151141</a>"},"_id":"34011","project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"department":[{"_id":"153"},{"_id":"880"}],"user_id":"41470","language":[{"iso":"eng"}],"publication":"Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022","type":"conference","status":"public"},{"publication":"IFAC-PapersOnLine","keyword":["Control and Systems Engineering"],"language":[{"iso":"eng"}],"quality_controlled":"1","issue":"12","year":"2022","publisher":"Elsevier BV","date_created":"2023-12-25T11:59:49Z","title":"Learning Data-Driven PCHD Models for Control Engineering Applications*","type":"journal_article","status":"public","_id":"50071","project":[{"_id":"690","name":"DART: Datengetriebene Methoden in der Regelungstechnik"}],"department":[{"_id":"153"},{"_id":"880"}],"user_id":"41470","publication_identifier":{"issn":["2405-8963"]},"publication_status":"published","intvolume":"        55","page":"389-394","citation":{"ama":"Junker A, Timmermann J, Trächtler A. Learning Data-Driven PCHD Models for Control Engineering Applications*. <i>IFAC-PapersOnLine</i>. 2022;55(12):389-394. doi:<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.343\">10.1016/j.ifacol.2022.07.343</a>","chicago":"Junker, Annika, Julia Timmermann, and Ansgar Trächtler. “Learning Data-Driven PCHD Models for Control Engineering Applications*.” <i>IFAC-PapersOnLine</i> 55, no. 12 (2022): 389–94. <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.343\">https://doi.org/10.1016/j.ifacol.2022.07.343</a>.","ieee":"A. Junker, J. Timmermann, and A. Trächtler, “Learning Data-Driven PCHD Models for Control Engineering Applications*,” <i>IFAC-PapersOnLine</i>, vol. 55, no. 12, pp. 389–394, 2022, doi: <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.343\">10.1016/j.ifacol.2022.07.343</a>.","apa":"Junker, A., Timmermann, J., &#38; Trächtler, A. (2022). Learning Data-Driven PCHD Models for Control Engineering Applications*. <i>IFAC-PapersOnLine</i>, <i>55</i>(12), 389–394. <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.343\">https://doi.org/10.1016/j.ifacol.2022.07.343</a>","short":"A. Junker, J. Timmermann, A. Trächtler, IFAC-PapersOnLine 55 (2022) 389–394.","mla":"Junker, Annika, et al. “Learning Data-Driven PCHD Models for Control Engineering Applications*.” <i>IFAC-PapersOnLine</i>, vol. 55, no. 12, Elsevier BV, 2022, pp. 389–94, doi:<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.343\">10.1016/j.ifacol.2022.07.343</a>.","bibtex":"@article{Junker_Timmermann_Trächtler_2022, title={Learning Data-Driven PCHD Models for Control Engineering Applications*}, volume={55}, DOI={<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.343\">10.1016/j.ifacol.2022.07.343</a>}, number={12}, journal={IFAC-PapersOnLine}, publisher={Elsevier BV}, author={Junker, Annika and Timmermann, Julia and Trächtler, Ansgar}, year={2022}, pages={389–394} }"},"oa":"1","date_updated":"2026-04-01T06:15:18Z","volume":55,"author":[{"id":"41470","full_name":"Junker, Annika","last_name":"Junker","orcid":"0009-0002-6475-2503","first_name":"Annika"},{"first_name":"Julia","full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann"},{"last_name":"Trächtler","id":"552","full_name":"Trächtler, Ansgar","first_name":"Ansgar"}],"doi":"10.1016/j.ifacol.2022.07.343","main_file_link":[{"url":"https://doi.org/10.1016/j.ifacol.2022.07.343","open_access":"1"}]}]