{"language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://ieeexplore.ieee.org/document/9836980"}],"date_updated":"2023-04-27T12:09:20Z","type":"conference","publication":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)","conference":{"name":"2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)","location":"Cairo, Egypt","start_date":"2022-05-10","end_date":"2022-05-12"},"author":[{"first_name":"Annika","id":"41470","last_name":"Junker","full_name":"Junker, Annika"},{"id":"15402","last_name":"Timmermann","full_name":"Timmermann, Julia","first_name":"Julia"},{"last_name":"Trächtler","id":"552","full_name":"Trächtler, Ansgar","first_name":"Ansgar"}],"status":"public","year":"2022","user_id":"552","citation":{"bibtex":"@inproceedings{Junker_Timmermann_Trächtler, 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}, pages={1–9} }","short":"A. Junker, J. Timmermann, A. Trächtler, in: 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022), n.d., pp. 1–9.","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, n.d. <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, pp. 1–9, doi: <a href=\"https://doi.org/10.1109/AIRC56195.2022.9836980\">10.1109/AIRC56195.2022.9836980</a>.","mla":"Junker, Annika, et al. “Data-Driven Models for Control Engineering Applications Using the Koopman Operator.” <i>2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)</i>, pp. 1–9, doi:<a href=\"https://doi.org/10.1109/AIRC56195.2022.9836980\">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>. ; :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. (n.d.). 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>"},"title":"Data-Driven Models for Control Engineering Applications Using the Koopman Operator","_id":"26389","department":[{"_id":"153"}],"publication_identifier":{"isbn":["978-1-6654-5946-4"]},"publication_status":"accepted","page":"1-9","doi":"10.1109/AIRC56195.2022.9836980","quality_controlled":"1","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"}],"oa":"1","date_created":"2021-10-18T05:59:07Z","keyword":["Koopman Operator","Nonlinear Control","Extended Dynamic Mode Decomposition","Hybrid Modelling"]}