[{"publication_identifier":{"issn":["1617-7061","1617-7061"]},"publication_status":"published","intvolume":"        25","citation":{"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} }","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>.","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>","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>","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>.","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>."},"volume":25,"author":[{"full_name":"Hesse, Michael","id":"29222","last_name":"Hesse","first_name":"Michael"},{"first_name":"Luis","last_name":"Schwarzer","full_name":"Schwarzer, Luis"},{"first_name":"Julia","last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia"},{"first_name":"Ansgar","id":"552","full_name":"Trächtler, Ansgar","last_name":"Trächtler"}],"date_updated":"2025-09-03T10:35:24Z","doi":"10.1002/pamm.70004","type":"journal_article","status":"public","department":[{"_id":"880"},{"_id":"153"}],"user_id":"15402","_id":"59740","project":[{"_id":"690","name":"DART: Datengetriebene Methoden in der Regelungstechnik"}],"issue":"2","year":"2025","date_created":"2025-04-30T08:18:46Z","publisher":"Wiley","title":"Robust and Efficient Hybrid Optimal Control via Gaussian Process Regression and Multiple Shooting With Experimental Validation on a Double Pendulum on a Cart","publication":"PAMM","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>"}],"language":[{"iso":"eng"}]},{"title":"DART - Datengetriebene Methoden in der Regelungstechnik","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","publisher":"HNI Verlagsschriftenreihe","date_updated":"2026-04-01T06:14:00Z","oa":"1","date_created":"2025-09-03T09:35:35Z","author":[{"first_name":"Julia","full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann"},{"last_name":"Götte","full_name":"Götte, Ricarda-Samantha","id":"43992","first_name":"Ricarda-Samantha"},{"full_name":"Junker, Annika","id":"41470","orcid":"0009-0002-6475-2503","last_name":"Junker","first_name":"Annika"},{"first_name":"Michael","last_name":"Hesse","full_name":"Hesse, Michael","id":"29222"},{"full_name":"Schwarzer, Luis","last_name":"Schwarzer","first_name":"Luis"}],"volume":"Band 430","place":"Paderborn","year":"2025","citation":{"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} }","short":"J. Timmermann, R.-S. Götte, A. Junker, M. Hesse, L. Schwarzer, DART - Datengetriebene Methoden in der Regelungstechnik, 1. Auflage, HNI Verlagsschriftenreihe, Paderborn, 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>.","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>","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>","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>."},"publication_status":"published","edition":"1. Auflage","language":[{"iso":"ger"}],"_id":"61118","user_id":"41470","department":[{"_id":"880"},{"_id":"153"}],"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"},{"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.","lang":"eng"}],"status":"public","type":"book"},{"department":[{"_id":"153"},{"_id":"880"}],"user_id":"15402","_id":"57893","project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"language":[{"iso":"eng"}],"publication":"PAMM","type":"journal_article","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"}],"volume":25,"date_created":"2025-01-01T16:11:38Z","author":[{"full_name":"Junker, Annika","id":"41470","last_name":"Junker","orcid":"0009-0002-6475-2503","first_name":"Annika"},{"full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann","first_name":"Julia"},{"first_name":"Ansgar","last_name":"Trächtler","full_name":"Trächtler, Ansgar","id":"552"}],"date_updated":"2025-09-03T09:33:23Z","oa":"1","publisher":"Wiley","doi":"10.1002/pamm.202400154","main_file_link":[{"open_access":"1","url":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pamm.202400154"}],"title":"Adaptive Data‐Driven Models in Port‐Hamiltonian Form for Control Design","issue":"1","quality_controlled":"1","publication_identifier":{"issn":["1617-7061","1617-7061"]},"publication_status":"published","intvolume":"        25","citation":{"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>","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>","short":"A. Junker, J. Timmermann, A. Trächtler, PAMM 25 (2024).","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} }"},"year":"2024"},{"language":[{"iso":"eng"}],"project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"_id":"59051","user_id":"15402","department":[{"_id":"153"},{"_id":"880"}],"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","type":"journal_article","publication":"PAMM","title":"Online Learning With Joint State and Model Estimation","doi":"10.1002/pamm.202400080","publisher":"Wiley","date_updated":"2025-09-03T10:36:10Z","date_created":"2025-03-17T07:06:12Z","author":[{"first_name":"Ricarda-Samantha","last_name":"Götte","full_name":"Götte, Ricarda-Samantha","id":"43992"},{"first_name":"Julia","last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402"}],"volume":25,"year":"2024","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>","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} }","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).","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>","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>."},"intvolume":"        25","publication_status":"published","publication_identifier":{"issn":["1617-7061","1617-7061"]},"issue":"1"},{"date_updated":"2024-11-13T08:43:05Z","volume":56,"author":[{"id":"43992","full_name":"Götte, Ricarda-Samantha","last_name":"Götte","first_name":"Ricarda-Samantha"},{"full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann","first_name":"Julia"}],"date_created":"2022-12-01T07:17:00Z","title":"Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF","doi":"https://doi.org/10.1016/j.ifacol.2023.02.015","conference":{"name":"12th IFAC Symposium on Nonlinear Control Systems NOLCOS 2022","start_date":"2023-01-04","end_date":"2023-01-06","location":"Canberra, Australien"},"quality_controlled":"1","issue":"1","year":"2023","page":"85-90","intvolume":"        56","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>.","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>.","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>"},"_id":"34171","department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","keyword":["joint estimation","unscented transform","Kalman filter","sparsity","data-driven","compressed sensing"],"language":[{"iso":"eng"}],"publication":"12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)","type":"conference","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"},{"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"},"date_updated":"2024-11-13T08:42:37Z","author":[{"first_name":"Ricarda-Samantha","full_name":"Götte, Ricarda-Samantha","id":"43992","last_name":"Götte"},{"id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann","first_name":"Julia"}],"date_created":"2023-05-02T15:16:43Z","volume":56,"year":"2023","citation":{"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.","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.","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.","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.","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."},"page":"869-874","intvolume":"        56","quality_controlled":"1","issue":"2","keyword":["joint estimation","unscented Kalman filter","sparsity","Laplacian prior","regularized horseshoe","principal component analysis"],"language":[{"iso":"eng"}],"_id":"44326","user_id":"43992","department":[{"_id":"153"},{"_id":"880"}],"abstract":[{"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.","lang":"eng"}],"status":"public","type":"conference","publication":"IFAC-PapersOnLine"},{"publication":"Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023","type":"conference","status":"public","_id":"48482","department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","language":[{"iso":"eng"}],"quality_controlled":"1","year":"2023","page":"113-123","citation":{"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>.","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>.","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>","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>.","short":"R.-S. Götte, J.N. Klusmann, J. Timmermann, in: Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023, 2023, pp. 113–123.","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>"},"oa":"1","date_updated":"2024-11-13T08:42:53Z","date_created":"2023-10-26T08:11:25Z","author":[{"last_name":"Götte","full_name":"Götte, Ricarda-Samantha","id":"43992","first_name":"Ricarda-Samantha"},{"last_name":"Klusmann","full_name":"Klusmann, Jo Noel","first_name":"Jo Noel"},{"first_name":"Julia","last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402"}],"title":"Data-driven identification of disturbances using a sliding mode observer","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":[{"url":"https://www.ksp.kit.edu/site/books/e/10.5445/KSP/1000162754/","open_access":"1"}]},{"title":"Hybrid Optimal Control for Dynamical Systems using Gaussian Process Regression and Unscented Transform<sup>*</sup>","doi":"10.23919/ecc57647.2023.10178368","date_updated":"2024-11-13T08:43:40Z","publisher":"IEEE","author":[{"full_name":"Hesse, Michael","id":"29222","last_name":"Hesse","first_name":"Michael"},{"first_name":"Julia","id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann"},{"first_name":"Ansgar","last_name":"Trächtler","id":"552","full_name":"Trächtler, Ansgar"}],"date_created":"2023-10-25T13:56:34Z","year":"2023","citation":{"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>.","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>."},"publication_status":"published","quality_controlled":"1","language":[{"iso":"eng"}],"_id":"48476","user_id":"82875","department":[{"_id":"153"},{"_id":"880"}],"status":"public","type":"conference","publication":"2023 European Control Conference (ECC)"},{"user_id":"41470","department":[{"_id":"153"},{"_id":"880"}],"project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"_id":"50070","type":"journal_article","status":"public","author":[{"first_name":"Annika","full_name":"Junker, Annika","id":"41470","last_name":"Junker","orcid":"0009-0002-6475-2503"},{"last_name":"Pape","full_name":"Pape, Keno Egon Friedrich","id":"52024","first_name":"Keno Egon Friedrich"},{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"},{"last_name":"Trächtler","id":"552","full_name":"Trächtler, Ansgar","first_name":"Ansgar"}],"volume":56,"oa":"1","date_updated":"2024-11-13T12:28:18Z","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.ifacol.2023.12.094"}],"doi":"10.1016/j.ifacol.2023.12.094","publication_status":"published","publication_identifier":{"issn":["2405-8963"]},"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>","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>.","short":"A. Junker, K.E.F. Pape, J. Timmermann, A. Trächtler, IFAC-PapersOnLine 56 (2023) 625–630.","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} }","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>"},"page":"625-630","intvolume":"        56","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"},{"doi":"10.1109/irc55401.2022.00031","conference":{"end_date":"2022-12-07","location":"Naples, Italy","name":"2022 Sixth IEEE International Conference on Robotic Computing (IRC)","start_date":"2022-12-05"},"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/10023639"}],"title":"Autonomous Golf Putting with Data-Driven and Physics-Based Methods","author":[{"id":"41470","full_name":"Junker, Annika","orcid":"0009-0002-6475-2503","last_name":"Junker","first_name":"Annika"},{"full_name":"Fittkau, Niklas","id":"69890","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","full_name":"Trächtler, Ansgar","id":"552","last_name":"Trächtler"}],"date_created":"2023-02-20T08:10:39Z","date_updated":"2026-04-01T05:49:07Z","publisher":"IEEE","citation":{"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>.","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} }","short":"A. Junker, N. Fittkau, J. Timmermann, A. Trächtler, in: 2022 Sixth IEEE International Conference on Robotic Computing (IRC), IEEE, 2023.","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>","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>"},"year":"2023","quality_controlled":"1","publication_status":"published","language":[{"iso":"eng"}],"department":[{"_id":"153"},{"_id":"880"}],"user_id":"41470","_id":"42238","project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"status":"public","publication":"2022 Sixth IEEE International Conference on Robotic Computing (IRC)","type":"conference"},{"keyword":["data-driven","physics-based","physics-informed","neural networks","system identification","hybrid modelling"],"language":[{"iso":"eng"}],"_id":"26539","user_id":"43992","department":[{"_id":"153"},{"_id":"880"}],"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."}],"status":"public","type":"conference","publication":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)","title":"Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2112.08148"}],"conference":{"start_date":"2021-12-08","name":"3rd International Conference on Artificial Intelligence, Robotics and Control","location":"Cairo, Egypt","end_date":"2021-12-10"},"doi":"10.1109/AIRC56195.2022.9836982","oa":"1","date_updated":"2024-11-13T08:43:28Z","author":[{"last_name":"Götte","full_name":"Götte, Ricarda-Samantha","id":"43992","first_name":"Ricarda-Samantha"},{"first_name":"Julia","full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann"}],"date_created":"2021-10-19T14:47:17Z","year":"2022","citation":{"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>","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>.","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} }","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.","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>"},"page":"67-76","quality_controlled":"1"},{"doi":"https://doi.org/10.1016/j.ifacol.2022.07.282","conference":{"name":"14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)","start_date":"2022-06-29","end_date":"2022-07-01","location":"Casablanca, Morocco"},"date_updated":"2024-11-13T08:43:16Z","author":[{"first_name":"Oliver","last_name":"Schön","full_name":"Schön, Oliver"},{"id":"43992","full_name":"Götte, Ricarda-Samantha","last_name":"Götte","first_name":"Ricarda-Samantha"},{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"}],"volume":55,"citation":{"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>.","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} }","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>.","short":"O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 2022, pp. 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>"},"intvolume":"        55","page":"19-24","_id":"31066","user_id":"43992","department":[{"_id":"153"},{"_id":"880"}],"status":"public","type":"conference","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":[{"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. ","lang":"eng"}],"publication":"14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)"},{"publication_identifier":{"isbn":["978-989-758-549-4"]},"quality_controlled":"1","year":"2022","page":"383-394","citation":{"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.","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} }","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.","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.","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.","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.","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."},"date_updated":"2024-11-13T08:44:17Z","author":[{"last_name":"Hesse","full_name":"Hesse, Michael","id":"29222","first_name":"Michael"},{"first_name":"Matthias","last_name":"Hunstig","full_name":"Hunstig, Matthias"},{"full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann","first_name":"Julia"},{"id":"552","full_name":"Trächtler, Ansgar","last_name":"Trächtler","first_name":"Ansgar"}],"date_created":"2022-02-09T12:50:25Z","title":"Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design","conference":{"end_date":"2022-02-05","location":"Online","name":"11th International Conference on Pattern Recognition Applications and Methods","start_date":"2022-02-03"},"publication":"Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)","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","_id":"29803","department":[{"_id":"153"},{"_id":"880"}],"user_id":"82875","keyword":["Bayesian optimization","Wire bonding","Feed-forward control","model-free design"],"language":[{"iso":"eng"}]},{"quality_controlled":"1","publication_identifier":{"isbn":["978-1-6654-5946-4"]},"publication_status":"published","year":"2022","page":"1-9","citation":{"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>","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} }","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>.","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>"},"date_updated":"2026-04-01T05:51:06Z","author":[{"last_name":"Junker","orcid":"0009-0002-6475-2503","full_name":"Junker, Annika","id":"41470","first_name":"Annika"},{"id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann","first_name":"Julia"},{"first_name":"Ansgar","id":"552","full_name":"Trächtler, Ansgar","last_name":"Trächtler"}],"date_created":"2021-10-18T05:59:07Z","title":"Data-Driven Models for Control Engineering Applications Using the Koopman Operator","doi":"10.1109/AIRC56195.2022.9836980","conference":{"end_date":"2022-05-12","location":"Cairo, Egypt","name":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)","start_date":"2022-05-10"},"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9836980"}],"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","_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"}]},{"oa":"1","date_updated":"2026-04-01T05:59:13Z","date_created":"2022-11-04T10:08:39Z","author":[{"id":"41470","full_name":"Junker, Annika","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","last_name":"Trächtler","full_name":"Trächtler, Ansgar","id":"552"}],"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":{"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>.","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>","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} }","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>.","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>"},"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"},{"year":"2022","quality_controlled":"1","issue":"12","title":"Learning Data-Driven PCHD Models for Control Engineering Applications*","publisher":"Elsevier BV","date_created":"2023-12-25T11:59:49Z","publication":"IFAC-PapersOnLine","keyword":["Control and Systems Engineering"],"language":[{"iso":"eng"}],"intvolume":"        55","page":"389-394","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.","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} }","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>.","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>"},"publication_identifier":{"issn":["2405-8963"]},"publication_status":"published","doi":"10.1016/j.ifacol.2022.07.343","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.ifacol.2022.07.343"}],"oa":"1","date_updated":"2026-04-01T06:15:18Z","volume":55,"author":[{"id":"41470","full_name":"Junker, Annika","orcid":"0009-0002-6475-2503","last_name":"Junker","first_name":"Annika"},{"full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann","first_name":"Julia"},{"first_name":"Ansgar","id":"552","full_name":"Trächtler, Ansgar","last_name":"Trächtler"}],"status":"public","type":"journal_article","_id":"50071","project":[{"_id":"690","name":"DART: Datengetriebene Methoden in der Regelungstechnik"}],"department":[{"_id":"153"},{"_id":"880"}],"user_id":"41470"},{"user_id":"24876","department":[{"_id":"153"}],"_id":"22984","language":[{"iso":"eng"}],"type":"book_chapter","publication":"Steigerung der Intelligenz mechatronischer Systeme","status":"public","author":[{"first_name":"Christopher","last_name":"Lüke","id":"22675","full_name":"Lüke, Christopher"},{"id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann","first_name":"Julia"},{"last_name":"Kessler","full_name":"Kessler, Jan Henning","first_name":"Jan Henning"},{"first_name":"Ansgar","id":"552","full_name":"Trächtler, Ansgar","last_name":"Trächtler"}],"date_created":"2021-08-09T05:39:28Z","volume":1,"date_updated":"2022-01-06T06:55:44Z","publisher":"Springer Vieweg","title":"Intelligente Steuerungen und Regelungen","citation":{"mla":"Lüke, Christopher, et al. “Intelligente Steuerungen Und Regelungen.” <i>Steigerung Der Intelligenz Mechatronischer Systeme</i>, vol. 1, Springer Vieweg, 2018, pp. 153–92.","short":"C. Lüke, J. Timmermann, J.H. Kessler, A. Trächtler, in: Steigerung Der Intelligenz Mechatronischer Systeme, Springer Vieweg, 2018, pp. 153–192.","bibtex":"@inbook{Lüke_Timmermann_Kessler_Trächtler_2018, title={Intelligente Steuerungen und Regelungen}, volume={1}, booktitle={Steigerung der Intelligenz mechatronischer Systeme}, publisher={Springer Vieweg}, author={Lüke, Christopher and Timmermann, Julia and Kessler, Jan Henning and Trächtler, Ansgar}, year={2018}, pages={153–192} }","apa":"Lüke, C., Timmermann, J., Kessler, J. H., &#38; Trächtler, A. (2018). Intelligente Steuerungen und Regelungen. In <i>Steigerung der Intelligenz mechatronischer Systeme</i> (Vol. 1, pp. 153–192). Springer Vieweg.","chicago":"Lüke, Christopher, Julia Timmermann, Jan Henning Kessler, and Ansgar Trächtler. “Intelligente Steuerungen Und Regelungen.” In <i>Steigerung Der Intelligenz Mechatronischer Systeme</i>, 1:153–92. Springer Vieweg, 2018.","ieee":"C. Lüke, J. Timmermann, J. H. Kessler, and A. Trächtler, “Intelligente Steuerungen und Regelungen,” in <i>Steigerung der Intelligenz mechatronischer Systeme</i>, vol. 1, Springer Vieweg, 2018, pp. 153–192.","ama":"Lüke C, Timmermann J, Kessler JH, Trächtler A. Intelligente Steuerungen und Regelungen. In: <i>Steigerung Der Intelligenz Mechatronischer Systeme</i>. Vol 1. Springer Vieweg; 2018:153-192."},"intvolume":"         1","page":"153-192","year":"2018"},{"language":[{"iso":"eng"}],"_id":"22996","user_id":"29222","department":[{"_id":"153"}],"abstract":[{"text":"The effective control design of a dynamical system traditionally relies on a high level of system understanding, usually expressed in terms of an exact physical model. In contrast to this, reinforcement learning adopts a data-driven approach and constructs an optimal control strategy by interacting with the underlying system. To keep the wear of real-world systems as low as possible, the learning process should be short. In our research, we used the state-of-the-art reinforcement learning method PILCO to design a feedback control strategy for the swing-up of the double pendulum on a cart with remarkably few test iterations at the test bench. PILCO stands for “probabilistic inference for learning control” and requires only few expert knowledge for learning. To achieve the swing-up of a double pendulum on a cart to its upper unstable equilibrium position, we introduce additional state restrictions to PILCO, so that the limited cart distance can be taken into account. Thanks to these measures, we were able to learn the swing up at the real test bench for the first time and in only 27 learning iterations.","lang":"eng"}],"status":"public","type":"journal_article","publication":"Procedia Manufacturing","title":"A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart","date_updated":"2023-11-06T15:17:24Z","date_created":"2021-08-09T05:41:38Z","author":[{"last_name":"Hesse","full_name":"Hesse, Michael","id":"29222","first_name":"Michael"},{"id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann","first_name":"Julia"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"},{"first_name":"Ansgar","last_name":"Trächtler","id":"552","full_name":"Trächtler, Ansgar"}],"volume":24,"year":"2018","citation":{"ama":"Hesse M, Timmermann J, Hüllermeier E, Trächtler A. A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. <i>Procedia Manufacturing</i>. 2018;24:15-20.","chicago":"Hesse, Michael, Julia Timmermann, Eyke Hüllermeier, and Ansgar Trächtler. “A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart.” <i>Procedia Manufacturing</i> 24 (2018): 15–20.","ieee":"M. Hesse, J. Timmermann, E. Hüllermeier, and A. Trächtler, “A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart,” <i>Procedia Manufacturing</i>, vol. 24, pp. 15–20, 2018.","apa":"Hesse, M., Timmermann, J., Hüllermeier, E., &#38; Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. <i>Procedia Manufacturing</i>, <i>24</i>, 15–20.","bibtex":"@article{Hesse_Timmermann_Hüllermeier_Trächtler_2018, title={A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart}, volume={24}, journal={Procedia Manufacturing}, author={Hesse, Michael and Timmermann, Julia and Hüllermeier, Eyke and Trächtler, Ansgar}, year={2018}, pages={15–20} }","mla":"Hesse, Michael, et al. “A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart.” <i>Procedia Manufacturing</i>, vol. 24, 2018, pp. 15–20.","short":"M. Hesse, J. Timmermann, E. Hüllermeier, A. Trächtler, Procedia Manufacturing 24 (2018) 15–20."},"page":"15 - 20","intvolume":"        24","quality_controlled":"1"},{"author":[{"first_name":"Ke","last_name":"Xu","full_name":"Xu, Ke"},{"first_name":"Julia","id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann"},{"last_name":"Trächtler","full_name":"Trächtler, Ansgar","id":"552","first_name":"Ansgar"}],"date_created":"2021-08-09T05:50:11Z","date_updated":"2022-01-06T06:55:45Z","publisher":"IEEE","title":"Nonlinear Model Predictive Control with Discrete Mechanics and Optimal Control","citation":{"bibtex":"@inproceedings{Xu_Timmermann_Trächtler_2017, title={Nonlinear Model Predictive Control with Discrete Mechanics and Optimal Control}, booktitle={Proc. Advanced Intelligent Mechatronics (AIM)}, publisher={IEEE}, author={Xu, Ke and Timmermann, Julia and Trächtler, Ansgar}, year={2017} }","short":"K. Xu, J. Timmermann, A. Trächtler, in: Proc. Advanced Intelligent Mechatronics (AIM), IEEE, 2017.","mla":"Xu, Ke, et al. “Nonlinear Model Predictive Control with Discrete Mechanics and Optimal Control.” <i>Proc. Advanced Intelligent Mechatronics (AIM)</i>, IEEE, 2017.","apa":"Xu, K., Timmermann, J., &#38; Trächtler, A. (2017). Nonlinear Model Predictive Control with Discrete Mechanics and Optimal Control. In <i>Proc. Advanced Intelligent Mechatronics (AIM)</i>. IEEE.","ama":"Xu K, Timmermann J, Trächtler A. Nonlinear Model Predictive Control with Discrete Mechanics and Optimal Control. In: <i>Proc. Advanced Intelligent Mechatronics (AIM)</i>. IEEE; 2017.","chicago":"Xu, Ke, Julia Timmermann, and Ansgar Trächtler. “Nonlinear Model Predictive Control with Discrete Mechanics and Optimal Control.” In <i>Proc. Advanced Intelligent Mechatronics (AIM)</i>. IEEE, 2017.","ieee":"K. Xu, J. Timmermann, and A. Trächtler, “Nonlinear Model Predictive Control with Discrete Mechanics and Optimal Control,” in <i>Proc. Advanced Intelligent Mechatronics (AIM)</i>, 2017."},"year":"2017","department":[{"_id":"153"}],"user_id":"24876","_id":"23005","language":[{"iso":"eng"}],"publication":"Proc. Advanced Intelligent Mechatronics (AIM)","type":"conference","status":"public"},{"language":[{"iso":"eng"}],"_id":"23006","user_id":"24876","department":[{"_id":"153"}],"status":"public","type":"conference","publication":"Proc. 20th IFAC World Congress","title":"Swing-up of the moving double pendulum on a cart with simulation based LQR-Trees","date_updated":"2022-01-06T06:55:45Z","date_created":"2021-08-09T05:50:12Z","author":[{"first_name":"Ke","full_name":"Xu, Ke","last_name":"Xu"},{"last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402","first_name":"Julia"},{"first_name":"Ansgar","full_name":"Trächtler, Ansgar","id":"552","last_name":"Trächtler"}],"year":"2017","citation":{"ama":"Xu K, Timmermann J, Trächtler A. Swing-up of the moving double pendulum on a cart with simulation based LQR-Trees. In: <i>Proc. 20th IFAC World Congress</i>. ; 2017.","chicago":"Xu, Ke, Julia Timmermann, and Ansgar Trächtler. “Swing-up of the Moving Double Pendulum on a Cart with Simulation Based LQR-Trees.” In <i>Proc. 20th IFAC World Congress</i>, 2017.","ieee":"K. Xu, J. Timmermann, and A. Trächtler, “Swing-up of the moving double pendulum on a cart with simulation based LQR-Trees,” in <i>Proc. 20th IFAC World Congress</i>, 2017.","mla":"Xu, Ke, et al. “Swing-up of the Moving Double Pendulum on a Cart with Simulation Based LQR-Trees.” <i>Proc. 20th IFAC World Congress</i>, 2017.","bibtex":"@inproceedings{Xu_Timmermann_Trächtler_2017, title={Swing-up of the moving double pendulum on a cart with simulation based LQR-Trees}, booktitle={Proc. 20th IFAC World Congress}, author={Xu, Ke and Timmermann, Julia and Trächtler, Ansgar}, year={2017} }","short":"K. Xu, J. Timmermann, A. Trächtler, in: Proc. 20th IFAC World Congress, 2017.","apa":"Xu, K., Timmermann, J., &#38; Trächtler, A. (2017). Swing-up of the moving double pendulum on a cart with simulation based LQR-Trees. In <i>Proc. 20th IFAC World Congress</i>."}}]