[{"abstract":[{"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.","lang":"eng"}],"status":"public","type":"conference","publication":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)","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"}],"year":"2022","citation":{"short":"R.-S. Götte, J. Timmermann, in: 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC), 2022, pp. 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>.","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>","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>","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>.","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>."},"page":"67-76","quality_controlled":"1","title":"Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering","main_file_link":[{"url":"https://arxiv.org/abs/2112.08148","open_access":"1"}],"conference":{"name":"3rd International Conference on Artificial Intelligence, Robotics and Control","start_date":"2021-12-08","end_date":"2021-12-10","location":"Cairo, Egypt"},"doi":"10.1109/AIRC56195.2022.9836982","date_updated":"2024-11-13T08:43:28Z","oa":"1","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","full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann"}]},{"language":[{"iso":"eng"}],"keyword":["Koopman Operator","Nonlinear Control","Extended Dynamic Mode Decomposition","Hybrid Modelling"],"user_id":"41470","department":[{"_id":"153"},{"_id":"880"}],"project":[{"name":"DART: Datengetriebene Methoden in der Regelungstechnik","_id":"690"}],"_id":"26389","status":"public","abstract":[{"text":"Within this work, we investigate how data-driven numerical approximation methods of the Koopman operator can be used in practical control engineering applications. We refer to the method Extended Dynamic Mode Decomposition (EDMD), which approximates a nonlinear dynamical system as a linear model. This makes the method ideal for control engineering applications, because a linear system description is often assumed for this purpose. Using academic  examples, we simulatively analyze the prediction performance of the learned EDMD models and show how relevant system properties like stability, controllability, and observability are reflected by the EDMD model, which is a critical requirement for a successful control design process. Subsequently, we present our experimental results on a mechatronic test bench and evaluate the applicability to the control engineering design process. As a result, the investigated methods are suitable as a low-effort alternative for the design steps of model building and adaptation in the classical model-based controller design method.","lang":"eng"}],"type":"conference","publication":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9836980"}],"conference":{"name":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)","start_date":"2022-05-10","end_date":"2022-05-12","location":"Cairo, Egypt"},"doi":"10.1109/AIRC56195.2022.9836980","title":"Data-Driven Models for Control Engineering Applications Using the Koopman Operator","date_created":"2021-10-18T05:59:07Z","author":[{"id":"41470","full_name":"Junker, Annika","orcid":"0009-0002-6475-2503","last_name":"Junker","first_name":"Annika"},{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"},{"id":"552","full_name":"Trächtler, Ansgar","last_name":"Trächtler","first_name":"Ansgar"}],"date_updated":"2026-04-01T05:51:06Z","citation":{"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>.","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>","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>"},"page":"1-9","year":"2022","publication_status":"published","quality_controlled":"1","publication_identifier":{"isbn":["978-1-6654-5946-4"]}}]