[{"year":"2025","issue":"2","title":"Robust and Efficient Hybrid Optimal Control via Gaussian Process Regression and Multiple Shooting With Experimental Validation on a Double Pendulum on a Cart","date_created":"2025-04-30T08:18:46Z","publisher":"Wiley","abstract":[{"lang":"eng","text":"<jats:title>ABSTRACT</jats:title><jats:p>In 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 hybrid<jats:sup>1</jats:sup> 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.</jats:p>"}],"publication":"PAMM","language":[{"iso":"eng"}],"citation":{"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.” <i>PAMM</i>, vol. 25, no. 2, Wiley, 2025, doi:<a href=\"https://doi.org/10.1002/pamm.70004\">10.1002/pamm.70004</a>.","short":"M. Hesse, L. Schwarzer, J. Timmermann, A. Trächtler, PAMM 25 (2025).","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={<a href=\"https://doi.org/10.1002/pamm.70004\">10.1002/pamm.70004</a>}, number={2}, journal={PAMM}, publisher={Wiley}, author={Hesse, Michael and Schwarzer, Luis and Timmermann, Julia and Trächtler, Ansgar}, year={2025} }","apa":"Hesse, M., Schwarzer, L., Timmermann, J., &#38; 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. <i>PAMM</i>, <i>25</i>(2). <a href=\"https://doi.org/10.1002/pamm.70004\">https://doi.org/10.1002/pamm.70004</a>","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.” <i>PAMM</i> 25, no. 2 (2025). <a href=\"https://doi.org/10.1002/pamm.70004\">https://doi.org/10.1002/pamm.70004</a>.","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,” <i>PAMM</i>, vol. 25, no. 2, 2025, doi: <a href=\"https://doi.org/10.1002/pamm.70004\">10.1002/pamm.70004</a>.","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. <i>PAMM</i>. 2025;25(2). doi:<a href=\"https://doi.org/10.1002/pamm.70004\">10.1002/pamm.70004</a>"},"intvolume":"        25","publication_status":"published","publication_identifier":{"issn":["1617-7061","1617-7061"]},"doi":"10.1002/pamm.70004","author":[{"first_name":"Michael","id":"29222","full_name":"Hesse, Michael","last_name":"Hesse"},{"full_name":"Schwarzer, Luis","last_name":"Schwarzer","first_name":"Luis"},{"first_name":"Julia","full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann"},{"first_name":"Ansgar","id":"552","full_name":"Trächtler, Ansgar","last_name":"Trächtler"}],"volume":25,"date_updated":"2025-09-03T10:35:24Z","status":"public","type":"journal_article","user_id":"15402","department":[{"_id":"880"},{"_id":"153"}],"project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"_id":"59740"},{"date_created":"2025-09-03T09:35:35Z","author":[{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"},{"last_name":"Götte","id":"43992","full_name":"Götte, Ricarda-Samantha","first_name":"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","publisher":"HNI Verlagsschriftenreihe","oa":"1","date_updated":"2026-04-01T06:14:00Z","main_file_link":[{"open_access":"1","url":"https://digital.ub.uni-paderborn.de/doi/10.17619/UNIPB/1-2305"}],"doi":"10.17619/UNIPB/1-2305","title":"DART - Datengetriebene Methoden in der Regelungstechnik","edition":"1. Auflage","publication_status":"published","citation":{"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>","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>.","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} }","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>.","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."},"place":"Paderborn","year":"2025","user_id":"41470","department":[{"_id":"880"},{"_id":"153"}],"_id":"61118","language":[{"iso":"ger"}],"type":"book","status":"public","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."}]},{"keyword":["state estimation","joint estimation","sparsity"],"language":[{"iso":"ger"}],"_id":"56940","user_id":"43992","series_title":"Verlagsschriftenreihe des Heinz Nixdorf Instituts","department":[{"_id":"880"},{"_id":"153"}],"abstract":[{"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":"ger"},{"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.","lang":"eng"}],"status":"public","type":"dissertation","title":"Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit","doi":"10.17619/UNIPB/1-2066","date_updated":"2024-11-07T11:47:59Z","date_created":"2024-11-07T11:43:05Z","author":[{"first_name":"Ricarda-Samantha","last_name":"Götte","id":"43992","full_name":"Götte, Ricarda-Samantha"}],"supervisor":[{"first_name":"Julia","full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann"},{"full_name":"Mikut, Ralf","last_name":"Mikut","first_name":"Ralf"}],"volume":423,"year":"2024","citation":{"ieee":"R.-S. Götte, <i>Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit</i>, vol. 423. 2024.","chicago":"Götte, Ricarda-Samantha. <i>Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit</i>. Vol. 423. Verlagsschriftenreihe des Heinz Nixdorf Instituts, 2024. <a href=\"https://doi.org/10.17619/UNIPB/1-2066\">https://doi.org/10.17619/UNIPB/1-2066</a>.","ama":"Götte R-S. <i>Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit</i>. Vol 423.; 2024. doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2066\">10.17619/UNIPB/1-2066</a>","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={<a href=\"https://doi.org/10.17619/UNIPB/1-2066\">10.17619/UNIPB/1-2066</a>}, author={Götte, Ricarda-Samantha}, year={2024}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts} }","mla":"Götte, Ricarda-Samantha. <i>Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit</i>. 2024, doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2066\">10.17619/UNIPB/1-2066</a>.","short":"R.-S. Götte, Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit, 2024.","apa":"Götte, R.-S. (2024). <i>Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit</i> (Vol. 423). <a href=\"https://doi.org/10.17619/UNIPB/1-2066\">https://doi.org/10.17619/UNIPB/1-2066</a>"},"intvolume":"       423","publication_status":"published","publication_identifier":{"isbn":["978-3-947647-42-2"]}},{"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."}],"status":"public","type":"dissertation","language":[{"iso":"ger"}],"_id":"58164","project":[{"_id":"690","name":"DART: Datengetriebene Methoden in der Regelungstechnik"}],"department":[{"_id":"153"},{"_id":"880"}],"user_id":"41470","series_title":"Verlagsschriftenreihe des Heinz Nixdorf Instituts","year":"2024","place":"Paderborn","citation":{"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>.","short":"A. Junker, Datengetriebene Modellbildung für nichtlineare mechatronische Systeme in regelungstechnisch verwertbarer Form, Heinz Nixdorf Institut, Paderborn, 2024.","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>","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>."},"publication_identifier":{"isbn":["9783947647477"]},"publication_status":"published","title":"Datengetriebene Modellbildung für nichtlineare mechatronische Systeme in regelungstechnisch verwertbarer Form","doi":"10.17619/UNIPB/1-2158","main_file_link":[{"url":"https://digital.ub.uni-paderborn.de/hs/download/pdf/7770359","open_access":"1"}],"oa":"1","publisher":"Heinz Nixdorf Institut","date_updated":"2025-01-16T13:15:20Z","volume":"Band 428","date_created":"2025-01-13T11:19:30Z","supervisor":[{"last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402","first_name":"Julia"},{"first_name":"Boris","full_name":"Lohmann, Boris","last_name":"Lohmann"}],"author":[{"first_name":"Annika","orcid":"0009-0002-6475-2503","last_name":"Junker","id":"41470","full_name":"Junker, Annika"}]},{"language":[{"iso":"ger"}],"abstract":[{"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"},{"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."}],"publisher":"Heinz Nixdorf Institut","date_created":"2025-01-30T14:45:46Z","title":"Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression","year":"2024","_id":"58448","department":[{"_id":"153"},{"_id":"880"}],"series_title":"Verlagsschriftenreihe des Heinz Nixdorf Instituts","user_id":"82875","type":"dissertation","status":"public","oa":"1","date_updated":"2025-01-30T14:58:46Z","volume":426,"author":[{"first_name":"Michael","last_name":"Hesse","full_name":"Hesse, Michael","id":"29222"}],"supervisor":[{"last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402","first_name":"Julia"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"doi":"10.17619/UNIPB/1-2135","main_file_link":[{"url":"https://digital.ub.uni-paderborn.de/doi/10.17619/UNIPB/1-2135","open_access":"1"}],"publication_identifier":{"eissn":["2365-4422"],"isbn":["978-3-947647-45-3"]},"publication_status":"published","place":"Paderborn","intvolume":"       426","citation":{"ama":"Hesse M. <i>Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression</i>. Vol 426. Heinz Nixdorf Institut; 2024. doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2135\">10.17619/UNIPB/1-2135</a>","chicago":"Hesse, Michael. <i>Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression</i>. Vol. 426. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, 2024. <a href=\"https://doi.org/10.17619/UNIPB/1-2135\">https://doi.org/10.17619/UNIPB/1-2135</a>.","ieee":"M. Hesse, <i>Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression</i>, vol. 426. Paderborn: Heinz Nixdorf Institut, 2024.","short":"M. Hesse, Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression, Heinz Nixdorf Institut, Paderborn, 2024.","mla":"Hesse, Michael. <i>Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression</i>. Heinz Nixdorf Institut, 2024, doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2135\">10.17619/UNIPB/1-2135</a>.","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={<a href=\"https://doi.org/10.17619/UNIPB/1-2135\">10.17619/UNIPB/1-2135</a>}, publisher={Heinz Nixdorf Institut}, author={Hesse, Michael}, year={2024}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts} }","apa":"Hesse, M. (2024). <i>Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression</i> (Vol. 426). Heinz Nixdorf Institut. <a href=\"https://doi.org/10.17619/UNIPB/1-2135\">https://doi.org/10.17619/UNIPB/1-2135</a>"}},{"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"}],"status":"public","type":"journal_article","publication":"PAMM","language":[{"iso":"eng"}],"project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"_id":"57893","user_id":"15402","department":[{"_id":"153"},{"_id":"880"}],"year":"2024","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>"},"intvolume":"        25","publication_status":"published","quality_controlled":"1","publication_identifier":{"issn":["1617-7061","1617-7061"]},"issue":"1","title":"Adaptive Data‐Driven Models in Port‐Hamiltonian Form for Control Design","main_file_link":[{"open_access":"1","url":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pamm.202400154"}],"doi":"10.1002/pamm.202400154","oa":"1","date_updated":"2025-09-03T09:33:23Z","publisher":"Wiley","author":[{"first_name":"Annika","full_name":"Junker, Annika","id":"41470","orcid":"0009-0002-6475-2503","last_name":"Junker"},{"last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402","first_name":"Julia"},{"last_name":"Trächtler","full_name":"Trächtler, Ansgar","id":"552","first_name":"Ansgar"}],"date_created":"2025-01-01T16:11:38Z","volume":25},{"year":"2024","intvolume":"        25","citation":{"apa":"Götte, R.-S., &#38; Timmermann, J. (2024). Online Learning With Joint State and Model Estimation. <i>PAMM</i>, <i>25</i>(1). <a href=\"https://doi.org/10.1002/pamm.202400080\">https://doi.org/10.1002/pamm.202400080</a>","mla":"Götte, Ricarda-Samantha, and Julia Timmermann. “Online Learning With Joint State and Model Estimation.” <i>PAMM</i>, vol. 25, no. 1, Wiley, 2024, doi:<a href=\"https://doi.org/10.1002/pamm.202400080\">10.1002/pamm.202400080</a>.","short":"R.-S. Götte, J. Timmermann, PAMM 25 (2024).","bibtex":"@article{Götte_Timmermann_2024, title={Online Learning With Joint State and Model Estimation}, volume={25}, DOI={<a href=\"https://doi.org/10.1002/pamm.202400080\">10.1002/pamm.202400080</a>}, number={1}, journal={PAMM}, publisher={Wiley}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2024} }","ieee":"R.-S. Götte and J. Timmermann, “Online Learning With Joint State and Model Estimation,” <i>PAMM</i>, vol. 25, no. 1, 2024, doi: <a href=\"https://doi.org/10.1002/pamm.202400080\">10.1002/pamm.202400080</a>.","chicago":"Götte, Ricarda-Samantha, and Julia Timmermann. “Online Learning With Joint State and Model Estimation.” <i>PAMM</i> 25, no. 1 (2024). <a href=\"https://doi.org/10.1002/pamm.202400080\">https://doi.org/10.1002/pamm.202400080</a>.","ama":"Götte R-S, Timmermann J. Online Learning With Joint State and Model Estimation. <i>PAMM</i>. 2024;25(1). doi:<a href=\"https://doi.org/10.1002/pamm.202400080\">10.1002/pamm.202400080</a>"},"publication_identifier":{"issn":["1617-7061","1617-7061"]},"publication_status":"published","issue":"1","title":"Online Learning With Joint State and Model Estimation","doi":"10.1002/pamm.202400080","date_updated":"2025-09-03T10:36:10Z","publisher":"Wiley","volume":25,"date_created":"2025-03-17T07:06:12Z","author":[{"full_name":"Götte, Ricarda-Samantha","id":"43992","last_name":"Götte","first_name":"Ricarda-Samantha"},{"full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann","first_name":"Julia"}],"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.</jats:p>"}],"status":"public","publication":"PAMM","type":"journal_article","language":[{"iso":"eng"}],"_id":"59051","project":[{"_id":"690","name":"DART: Datengetriebene Methoden in der Regelungstechnik"}],"department":[{"_id":"153"},{"_id":"880"}],"user_id":"15402"},{"type":"conference","publication":"12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)","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."}],"status":"public","_id":"34171","user_id":"43992","department":[{"_id":"153"},{"_id":"880"}],"keyword":["joint estimation","unscented transform","Kalman filter","sparsity","data-driven","compressed sensing"],"language":[{"iso":"eng"}],"quality_controlled":"1","issue":"1","year":"2023","citation":{"apa":"Götte, R.-S., &#38; Timmermann, J. (2023). Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF. <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>, <i>56</i>(1), 85–90. <a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>","short":"R.-S. Götte, J. Timmermann, in: 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), 2023, pp. 85–90.","bibtex":"@inproceedings{Götte_Timmermann_2023, title={Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF}, volume={56}, DOI={<a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>}, number={1}, booktitle={12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2023}, pages={85–90} }","mla":"Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF.” <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>, vol. 56, no. 1, 2023, pp. 85–90, doi:<a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>.","ama":"Götte R-S, Timmermann J. Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF. In: <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>. Vol 56. ; 2023:85-90. doi:<a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>","ieee":"R.-S. Götte and J. Timmermann, “Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF,” in <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>, Canberra, Australien, 2023, vol. 56, no. 1, pp. 85–90, doi: <a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>.","chicago":"Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF.” In <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>, 56:85–90, 2023. <a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>."},"intvolume":"        56","page":"85-90","date_updated":"2024-11-13T08:43:05Z","date_created":"2022-12-01T07:17:00Z","author":[{"first_name":"Ricarda-Samantha","full_name":"Götte, Ricarda-Samantha","id":"43992","last_name":"Götte"},{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"}],"volume":56,"title":"Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF","conference":{"start_date":"2023-01-04","name":"12th IFAC Symposium on Nonlinear Control Systems NOLCOS 2022","location":"Canberra, Australien","end_date":"2023-01-06"},"doi":"https://doi.org/10.1016/j.ifacol.2023.02.015"},{"quality_controlled":"1","issue":"2","year":"2023","page":"869-874","intvolume":"        56","citation":{"mla":"Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF.” <i>IFAC-PapersOnLine</i>, vol. 56, no. 2, 2023, pp. 869–74.","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} }","short":"R.-S. Götte, J. Timmermann, in: IFAC-PapersOnLine, 2023, pp. 869–874.","apa":"Götte, R.-S., &#38; Timmermann, J. (2023). Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF. <i>IFAC-PapersOnLine</i>, <i>56</i>(2), 869–874.","ieee":"R.-S. Götte and J. Timmermann, “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF,” in <i>IFAC-PapersOnLine</i>, Yokohama, Japan, 2023, vol. 56, no. 2, pp. 869–874.","chicago":"Götte, Ricarda-Samantha, and Julia Timmermann. “Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF.” In <i>IFAC-PapersOnLine</i>, 56:869–74, 2023.","ama":"Götte R-S, Timmermann J. Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF. In: <i>IFAC-PapersOnLine</i>. Vol 56. ; 2023:869-874."},"date_updated":"2024-11-13T08:42:37Z","volume":56,"date_created":"2023-05-02T15:16:43Z","author":[{"full_name":"Götte, Ricarda-Samantha","id":"43992","last_name":"Götte","first_name":"Ricarda-Samantha"},{"full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann","first_name":"Julia"}],"title":"Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF","conference":{"location":"Yokohama, Japan","end_date":"2023-07-14","start_date":"2023-07-09","name":"22nd IFAC World Congress"},"publication":"IFAC-PapersOnLine","type":"conference","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."}],"status":"public","_id":"44326","department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","keyword":["joint estimation","unscented Kalman filter","sparsity","Laplacian prior","regularized horseshoe","principal component analysis"],"language":[{"iso":"eng"}]},{"page":"113-123","citation":{"ama":"Götte R-S, Klusmann JN, Timmermann J. Data-driven identification of disturbances using a sliding mode observer. In: <i>Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023</i>. ; 2023:113-123. doi:<a href=\"https://doi.org/10.5445/KSP/1000162754\">10.5445/KSP/1000162754</a>","chicago":"Götte, Ricarda-Samantha, Jo Noel Klusmann, and Julia Timmermann. “Data-Driven Identification of Disturbances Using a Sliding Mode Observer.” In <i>Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023</i>, 113–23, 2023. <a href=\"https://doi.org/10.5445/KSP/1000162754\">https://doi.org/10.5445/KSP/1000162754</a>.","ieee":"R.-S. Götte, J. N. Klusmann, and J. Timmermann, “Data-driven identification of disturbances using a sliding mode observer,” in <i>Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023</i>, Berlin, Germany, 2023, pp. 113–123, doi: <a href=\"https://doi.org/10.5445/KSP/1000162754\">10.5445/KSP/1000162754</a>.","short":"R.-S. Götte, J.N. Klusmann, J. Timmermann, in: Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023, 2023, pp. 113–123.","mla":"Götte, Ricarda-Samantha, et al. “Data-Driven Identification of Disturbances Using a Sliding Mode Observer.” <i>Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023</i>, 2023, pp. 113–23, doi:<a href=\"https://doi.org/10.5445/KSP/1000162754\">10.5445/KSP/1000162754</a>.","bibtex":"@inproceedings{Götte_Klusmann_Timmermann_2023, title={Data-driven identification of disturbances using a sliding mode observer}, DOI={<a href=\"https://doi.org/10.5445/KSP/1000162754\">10.5445/KSP/1000162754</a>}, 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} }","apa":"Götte, R.-S., Klusmann, J. N., &#38; Timmermann, J. (2023). Data-driven identification of disturbances using a sliding mode observer. <i>Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023</i>, 113–123. <a href=\"https://doi.org/10.5445/KSP/1000162754\">https://doi.org/10.5445/KSP/1000162754</a>"},"year":"2023","quality_controlled":"1","doi":"10.5445/KSP/1000162754","conference":{"start_date":"2023-11-23","name":"33. Workshop Computational Intelligence","location":"Berlin, Germany","end_date":"2023-11-24"},"main_file_link":[{"open_access":"1","url":"https://www.ksp.kit.edu/site/books/e/10.5445/KSP/1000162754/"}],"title":"Data-driven identification of disturbances using a sliding mode observer","author":[{"first_name":"Ricarda-Samantha","last_name":"Götte","full_name":"Götte, Ricarda-Samantha","id":"43992"},{"first_name":"Jo Noel","last_name":"Klusmann","full_name":"Klusmann, Jo Noel"},{"first_name":"Julia","last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402"}],"date_created":"2023-10-26T08:11:25Z","date_updated":"2024-11-13T08:42:53Z","oa":"1","status":"public","publication":"Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","_id":"48482"},{"doi":"10.23919/ecc57647.2023.10178368","title":"Hybrid Optimal Control for Dynamical Systems using Gaussian Process Regression and Unscented Transform<sup>*</sup>","date_created":"2023-10-25T13:56:34Z","author":[{"full_name":"Hesse, Michael","id":"29222","last_name":"Hesse","first_name":"Michael"},{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"},{"last_name":"Trächtler","id":"552","full_name":"Trächtler, Ansgar","first_name":"Ansgar"}],"date_updated":"2024-11-13T08:43:40Z","publisher":"IEEE","citation":{"ama":"Hesse M, Timmermann J, Trächtler A. Hybrid Optimal Control for Dynamical Systems using Gaussian Process Regression and Unscented Transform<sup>*</sup>. In: <i>2023 European Control Conference (ECC)</i>. IEEE; 2023. doi:<a href=\"https://doi.org/10.23919/ecc57647.2023.10178368\">10.23919/ecc57647.2023.10178368</a>","chicago":"Hesse, Michael, Julia Timmermann, and Ansgar Trächtler. “Hybrid Optimal Control for Dynamical Systems Using Gaussian Process Regression and Unscented Transform<sup>*</sup>.” In <i>2023 European Control Conference (ECC)</i>. IEEE, 2023. <a href=\"https://doi.org/10.23919/ecc57647.2023.10178368\">https://doi.org/10.23919/ecc57647.2023.10178368</a>.","ieee":"M. Hesse, J. Timmermann, and A. Trächtler, “Hybrid Optimal Control for Dynamical Systems using Gaussian Process Regression and Unscented Transform<sup>*</sup>,” 2023, doi: <a href=\"https://doi.org/10.23919/ecc57647.2023.10178368\">10.23919/ecc57647.2023.10178368</a>.","apa":"Hesse, M., Timmermann, J., &#38; Trächtler, A. (2023). Hybrid Optimal Control for Dynamical Systems using Gaussian Process Regression and Unscented Transform<sup>*</sup>. <i>2023 European Control Conference (ECC)</i>. <a href=\"https://doi.org/10.23919/ecc57647.2023.10178368\">https://doi.org/10.23919/ecc57647.2023.10178368</a>","bibtex":"@inproceedings{Hesse_Timmermann_Trächtler_2023, title={Hybrid Optimal Control for Dynamical Systems using Gaussian Process Regression and Unscented Transform<sup>*</sup>}, DOI={<a href=\"https://doi.org/10.23919/ecc57647.2023.10178368\">10.23919/ecc57647.2023.10178368</a>}, booktitle={2023 European Control Conference (ECC)}, publisher={IEEE}, author={Hesse, Michael and Timmermann, Julia and Trächtler, Ansgar}, year={2023} }","short":"M. Hesse, J. Timmermann, A. Trächtler, in: 2023 European Control Conference (ECC), IEEE, 2023.","mla":"Hesse, Michael, et al. “Hybrid Optimal Control for Dynamical Systems Using Gaussian Process Regression and Unscented Transform<sup>*</sup>.” <i>2023 European Control Conference (ECC)</i>, IEEE, 2023, doi:<a href=\"https://doi.org/10.23919/ecc57647.2023.10178368\">10.23919/ecc57647.2023.10178368</a>."},"year":"2023","publication_status":"published","quality_controlled":"1","language":[{"iso":"eng"}],"user_id":"82875","department":[{"_id":"153"},{"_id":"880"}],"_id":"48476","status":"public","type":"conference","publication":"2023 European Control Conference (ECC)"},{"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>","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>.","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} }","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>","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>."},"page":"625-630","intvolume":"        56","publication_status":"published","publication_identifier":{"issn":["2405-8963"]},"main_file_link":[{"url":"https://doi.org/10.1016/j.ifacol.2023.12.094","open_access":"1"}],"doi":"10.1016/j.ifacol.2023.12.094","oa":"1","date_updated":"2024-11-13T12:28:18Z","author":[{"first_name":"Annika","id":"41470","full_name":"Junker, Annika","orcid":"0009-0002-6475-2503","last_name":"Junker"},{"first_name":"Keno Egon Friedrich","last_name":"Pape","id":"52024","full_name":"Pape, Keno Egon Friedrich"},{"first_name":"Julia","id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann"},{"first_name":"Ansgar","last_name":"Trächtler","full_name":"Trächtler, Ansgar","id":"552"}],"volume":56,"status":"public","type":"journal_article","project":[{"_id":"690","name":"DART: Datengetriebene Methoden in der Regelungstechnik"}],"_id":"50070","user_id":"41470","department":[{"_id":"153"},{"_id":"880"}],"year":"2023","quality_controlled":"1","issue":"3","title":"Adaptive Koopman-Based Models for Holistic Controller and Observer Design","publisher":"Elsevier BV","date_created":"2023-12-25T11:55:19Z","publication":"IFAC-PapersOnLine","keyword":["General Medicine"],"language":[{"iso":"eng"}]},{"date_created":"2023-02-20T08:10:39Z","author":[{"first_name":"Annika","id":"41470","full_name":"Junker, Annika","orcid":"0009-0002-6475-2503","last_name":"Junker"},{"id":"69890","full_name":"Fittkau, Niklas","last_name":"Fittkau","orcid":"0009-0007-1281-4465","first_name":"Niklas"},{"first_name":"Julia","full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann"},{"first_name":"Ansgar","last_name":"Trächtler","id":"552","full_name":"Trächtler, Ansgar"}],"date_updated":"2026-04-01T05:49:07Z","publisher":"IEEE","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/10023639"}],"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","title":"Autonomous Golf Putting with Data-Driven and Physics-Based Methods","publication_status":"published","quality_controlled":"1","citation":{"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>.","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>","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>."},"year":"2023","user_id":"41470","department":[{"_id":"153"},{"_id":"880"}],"project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"_id":"42238","language":[{"iso":"eng"}],"type":"conference","publication":"2022 Sixth IEEE International Conference on Robotic Computing (IRC)","status":"public"},{"ddc":["490"],"keyword":["Technology Acceptance Model","Fachspezifische Chinesischsprachkurse","digitale Lehre","Moodle","Evaluation"],"language":[{"iso":"ger"}],"publication":" die hochschullehre. Interdisziplinäre Zeitschrift für Hochschule und Lehre","abstract":[{"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?“.","lang":"ger"}],"publisher":"wbv Publikation","date_created":"2023-04-06T09:59:20Z","title":"Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses für Studierende des Ingenieurwesens","quality_controlled":"1","issue":"8","year":"2022","_id":"43433","user_id":"32850","department":[{"_id":"669"},{"_id":"146"},{"_id":"398"}],"article_type":"original","alternative_title":["Evaluation of a digital subject-specific chinese language course for engineering students"],"type":"journal_article","status":"public","oa":"1","date_updated":"2023-04-27T08:59:48Z","author":[{"id":"32850","full_name":"Hambach, Dennis","last_name":"Hambach","first_name":"Dennis"}],"main_file_link":[{"url":"https://www.wbv.de/shop/Evaluation-eines-digitalen-Fachspezifischen-Chinesischsprachkurses-fuer-Studierende-des-Ingenieurwesens-HSL2249W","open_access":"1"}],"doi":"10.3278/HSL2249W","publication_status":"published","has_accepted_license":"1","citation":{"mla":"Hambach, Dennis. “Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses für Studierende des Ingenieurwesens.” <i> die hochschullehre. Interdisziplinäre Zeitschrift für Hochschule und Lehre</i>, no. 8, wbv Publikation, 2022, pp. 1–15, doi:<a href=\"https://doi.org/10.3278/HSL2249W\">10.3278/HSL2249W</a>.","bibtex":"@article{Hambach_2022, title={Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses für Studierende des Ingenieurwesens}, DOI={<a href=\"https://doi.org/10.3278/HSL2249W\">10.3278/HSL2249W</a>}, number={8}, journal={ die hochschullehre. Interdisziplinäre Zeitschrift für Hochschule und Lehre}, publisher={wbv Publikation}, author={Hambach, Dennis}, year={2022}, pages={1–15} }","short":"D. Hambach,  die hochschullehre. Interdisziplinäre Zeitschrift für Hochschule und Lehre (2022) 1–15.","apa":"Hambach, D. (2022). Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses für Studierende des Ingenieurwesens. <i> die hochschullehre. Interdisziplinäre Zeitschrift für Hochschule und Lehre</i>, <i>8</i>, 1–15. <a href=\"https://doi.org/10.3278/HSL2249W\">https://doi.org/10.3278/HSL2249W</a>","chicago":"Hambach, Dennis. “Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses für Studierende des Ingenieurwesens.” <i> die hochschullehre. Interdisziplinäre Zeitschrift für Hochschule und Lehre</i>, no. 8 (2022): 1–15. <a href=\"https://doi.org/10.3278/HSL2249W\">https://doi.org/10.3278/HSL2249W</a>.","ieee":"D. Hambach, “Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses für Studierende des Ingenieurwesens,” <i> die hochschullehre. Interdisziplinäre Zeitschrift für Hochschule und Lehre</i>, no. 8, pp. 1–15, 2022, doi: <a href=\"https://doi.org/10.3278/HSL2249W\">10.3278/HSL2249W</a>.","ama":"Hambach D. Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses für Studierende des Ingenieurwesens. <i> die hochschullehre Interdisziplinäre Zeitschrift für Hochschule und Lehre</i>. 2022;(8):1-15. doi:<a href=\"https://doi.org/10.3278/HSL2249W\">10.3278/HSL2249W</a>"},"page":"1-15"},{"status":"public","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."}],"publication":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)","type":"conference","language":[{"iso":"eng"}],"keyword":["data-driven","physics-based","physics-informed","neural networks","system identification","hybrid modelling"],"department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","_id":"26539","page":"67-76","citation":{"mla":"Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven System Identification for Non-Autonomous Systems in Control Engineering.” <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)</i>, 2022, pp. 67–76, doi:<a href=\"https://doi.org/10.1109/AIRC56195.2022.9836982\">10.1109/AIRC56195.2022.9836982</a>.","short":"R.-S. Götte, J. Timmermann, in: 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC), 2022, pp. 67–76.","bibtex":"@inproceedings{Götte_Timmermann_2022, title={Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering}, DOI={<a href=\"https://doi.org/10.1109/AIRC56195.2022.9836982\">10.1109/AIRC56195.2022.9836982</a>}, booktitle={2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2022}, pages={67–76} }","apa":"Götte, R.-S., &#38; Timmermann, J. (2022). Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering. <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)</i>, 67–76. <a href=\"https://doi.org/10.1109/AIRC56195.2022.9836982\">https://doi.org/10.1109/AIRC56195.2022.9836982</a>","chicago":"Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven System Identification for Non-Autonomous Systems in Control Engineering.” In <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)</i>, 67–76, 2022. <a href=\"https://doi.org/10.1109/AIRC56195.2022.9836982\">https://doi.org/10.1109/AIRC56195.2022.9836982</a>.","ieee":"R.-S. Götte and J. Timmermann, “Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering,” in <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)</i>, Cairo, Egypt, 2022, pp. 67–76, doi: <a href=\"https://doi.org/10.1109/AIRC56195.2022.9836982\">10.1109/AIRC56195.2022.9836982</a>.","ama":"Götte R-S, Timmermann J. Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering. In: <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)</i>. ; 2022:67-76. doi:<a href=\"https://doi.org/10.1109/AIRC56195.2022.9836982\">10.1109/AIRC56195.2022.9836982</a>"},"year":"2022","quality_controlled":"1","doi":"10.1109/AIRC56195.2022.9836982","conference":{"name":"3rd International Conference on Artificial Intelligence, Robotics and Control","start_date":"2021-12-08","end_date":"2021-12-10","location":"Cairo, Egypt"},"main_file_link":[{"url":"https://arxiv.org/abs/2112.08148","open_access":"1"}],"title":"Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering","date_created":"2021-10-19T14:47:17Z","author":[{"first_name":"Ricarda-Samantha","id":"43992","full_name":"Götte, Ricarda-Samantha","last_name":"Götte"},{"first_name":"Julia","last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402"}],"oa":"1","date_updated":"2024-11-13T08:43:28Z"},{"title":"Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems","date_created":"2022-05-05T06:22:55Z","year":"2022","quality_controlled":"1","issue":"12","keyword":["neural networks","physics-guided","data-driven","multi-objective optimization","system identification","machine learning","dynamical systems"],"language":[{"iso":"eng"}],"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. "}],"publication":"14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)","doi":"https://doi.org/10.1016/j.ifacol.2022.07.282","conference":{"start_date":"2022-06-29","name":"14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)","location":"Casablanca, Morocco","end_date":"2022-07-01"},"date_updated":"2024-11-13T08:43:16Z","volume":55,"author":[{"first_name":"Oliver","full_name":"Schön, Oliver","last_name":"Schön"},{"first_name":"Ricarda-Samantha","id":"43992","full_name":"Götte, Ricarda-Samantha","last_name":"Götte"},{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"}],"intvolume":"        55","page":"19-24","citation":{"short":"O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.","mla":"Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems.” <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, vol. 55, no. 12, 2022, pp. 19–24, doi:<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>.","bibtex":"@inproceedings{Schön_Götte_Timmermann_2022, title={Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}, volume={55}, DOI={<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>}, 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} }","apa":"Schön, O., Götte, R.-S., &#38; Timmermann, J. (2022). Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, <i>55</i>(12), 19–24. <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>","ama":"Schön O, Götte R-S, Timmermann J. Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. In: <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>. Vol 55. ; 2022:19-24. doi:<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>","ieee":"O. Schön, R.-S. Götte, and J. Timmermann, “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems,” in <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, Casablanca, Morocco, 2022, vol. 55, no. 12, pp. 19–24, doi: <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>.","chicago":"Schön, Oliver, Ricarda-Samantha Götte, and Julia Timmermann. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems.” In <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, 55:19–24, 2022. <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>."},"_id":"31066","department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","status":"public","type":"conference"},{"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."}],"status":"public","publication":"Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)","type":"conference","keyword":["Bayesian optimization","Wire bonding","Feed-forward control","model-free design"],"language":[{"iso":"eng"}],"_id":"29803","department":[{"_id":"153"},{"_id":"880"}],"user_id":"82875","year":"2022","page":"383-394","citation":{"mla":"Hesse, Michael, et al. “Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-Forward Control Design.” <i>Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)</i>, 2022, pp. 383–94.","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} }","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.","apa":"Hesse, M., Hunstig, M., Timmermann, J., &#38; Trächtler, A. (2022). Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design. <i>Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)</i>, 383–394.","chicago":"Hesse, Michael, Matthias Hunstig, Julia Timmermann, and Ansgar Trächtler. “Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-Forward Control Design.” In <i>Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)</i>, 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 <i>Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)</i>, Online, 2022, pp. 383–394.","ama":"Hesse M, Hunstig M, Timmermann J, Trächtler A. Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design. In: <i>Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)</i>. ; 2022:383-394."},"quality_controlled":"1","publication_identifier":{"isbn":["978-989-758-549-4"]},"title":"Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design","conference":{"name":"11th International Conference on Pattern Recognition Applications and Methods","start_date":"2022-02-03","end_date":"2022-02-05","location":"Online"},"date_updated":"2024-11-13T08:44:17Z","date_created":"2022-02-09T12:50:25Z","author":[{"first_name":"Michael","id":"29222","full_name":"Hesse, Michael","last_name":"Hesse"},{"first_name":"Matthias","full_name":"Hunstig, Matthias","last_name":"Hunstig"},{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"},{"first_name":"Ansgar","last_name":"Trächtler","id":"552","full_name":"Trächtler, Ansgar"}]},{"date_updated":"2026-04-01T05:51:06Z","date_created":"2021-10-18T05:59:07Z","author":[{"first_name":"Annika","orcid":"0009-0002-6475-2503","last_name":"Junker","full_name":"Junker, Annika","id":"41470"},{"first_name":"Julia","last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia"},{"last_name":"Trächtler","full_name":"Trächtler, Ansgar","id":"552","first_name":"Ansgar"}],"title":"Data-Driven Models for Control Engineering Applications Using the Koopman Operator","conference":{"start_date":"2022-05-10","name":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)","location":"Cairo, Egypt","end_date":"2022-05-12"},"doi":"10.1109/AIRC56195.2022.9836980","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9836980"}],"publication_identifier":{"isbn":["978-1-6654-5946-4"]},"quality_controlled":"1","publication_status":"published","year":"2022","page":"1-9","citation":{"ama":"Junker A, Timmermann J, Trächtler A. Data-Driven Models for Control Engineering Applications Using the Koopman Operator. In: <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)</i>. ; 2022:1-9. doi:<a href=\"https://doi.org/10.1109/AIRC56195.2022.9836980\">10.1109/AIRC56195.2022.9836980</a>","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>.","apa":"Junker, A., Timmermann, J., &#38; Trächtler, A. (2022). Data-Driven Models for Control Engineering Applications Using the Koopman Operator. <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)</i>, 1–9. <a href=\"https://doi.org/10.1109/AIRC56195.2022.9836980\">https://doi.org/10.1109/AIRC56195.2022.9836980</a>","bibtex":"@inproceedings{Junker_Timmermann_Trächtler_2022, title={Data-Driven Models for Control Engineering Applications Using the Koopman Operator}, DOI={<a href=\"https://doi.org/10.1109/AIRC56195.2022.9836980\">10.1109/AIRC56195.2022.9836980</a>}, booktitle={2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)}, author={Junker, Annika and Timmermann, Julia and Trächtler, Ansgar}, year={2022}, pages={1–9} }","short":"A. Junker, J. Timmermann, A. Trächtler, in: 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022), 2022, pp. 1–9.","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>."},"_id":"26389","project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"department":[{"_id":"153"},{"_id":"880"}],"user_id":"41470","keyword":["Koopman Operator","Nonlinear Control","Extended Dynamic Mode Decomposition","Hybrid Modelling"],"language":[{"iso":"eng"}],"publication":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)","type":"conference","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"}],"status":"public"},{"date_updated":"2026-04-01T05:59:13Z","oa":"1","date_created":"2022-11-04T10:08:39Z","author":[{"orcid":"0009-0002-6475-2503","last_name":"Junker","id":"41470","full_name":"Junker, Annika","first_name":"Annika"},{"orcid":"0009-0007-1281-4465","last_name":"Fittkau","id":"69890","full_name":"Fittkau, Niklas","first_name":"Niklas"},{"full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann","first_name":"Julia"},{"id":"552","full_name":"Trächtler, Ansgar","last_name":"Trächtler","first_name":"Ansgar"}],"title":"Autonomes Putten mittels datengetriebener und physikbasierter Methoden","main_file_link":[{"url":"https://publikationen.bibliothek.kit.edu/1000151141","open_access":"1"}],"doi":"10.5445/KSP/1000151141","conference":{"location":"Berlin, Germany","end_date":"2022-12-02","start_date":"2022-12-01","name":"32. Workshop Computational Intelligence"},"quality_controlled":"1","year":"2022","citation":{"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>","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>.","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>.","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.","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>.","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>"},"page":"119-124","project":[{"_id":"690","name":"DART: Datengetriebene Methoden in der Regelungstechnik"}],"_id":"34011","user_id":"41470","department":[{"_id":"153"},{"_id":"880"}],"language":[{"iso":"eng"}],"type":"conference","publication":"Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022","status":"public"},{"title":"Learning Data-Driven PCHD Models for Control Engineering Applications*","publisher":"Elsevier BV","date_created":"2023-12-25T11:59:49Z","year":"2022","quality_controlled":"1","issue":"12","keyword":["Control and Systems Engineering"],"language":[{"iso":"eng"}],"publication":"IFAC-PapersOnLine","main_file_link":[{"url":"https://doi.org/10.1016/j.ifacol.2022.07.343","open_access":"1"}],"doi":"10.1016/j.ifacol.2022.07.343","oa":"1","date_updated":"2026-04-01T06:15:18Z","author":[{"id":"41470","full_name":"Junker, Annika","orcid":"0009-0002-6475-2503","last_name":"Junker","first_name":"Annika"},{"first_name":"Julia","last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia"},{"id":"552","full_name":"Trächtler, Ansgar","last_name":"Trächtler","first_name":"Ansgar"}],"volume":55,"citation":{"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>.","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>.","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>","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} }","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>"},"page":"389-394","intvolume":"        55","publication_status":"published","publication_identifier":{"issn":["2405-8963"]},"project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"_id":"50071","user_id":"41470","department":[{"_id":"153"},{"_id":"880"}],"status":"public","type":"journal_article"}]