[{"language":[{"iso":"ger"}],"_id":"61118","department":[{"_id":"880"},{"_id":"153"}],"user_id":"41470","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","title":"DART - Datengetriebene Methoden in der Regelungstechnik","doi":"10.17619/UNIPB/1-2305","main_file_link":[{"open_access":"1","url":"https://digital.ub.uni-paderborn.de/doi/10.17619/UNIPB/1-2305"}],"publisher":"HNI Verlagsschriftenreihe","date_updated":"2026-04-01T06:14:00Z","oa":"1","volume":"Band 430","date_created":"2025-09-03T09:35:35Z","author":[{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"},{"last_name":"Götte","full_name":"Götte, Ricarda-Samantha","id":"43992","first_name":"Ricarda-Samantha"},{"last_name":"Junker","orcid":"0009-0002-6475-2503","id":"41470","full_name":"Junker, Annika","first_name":"Annika"},{"full_name":"Hesse, Michael","id":"29222","last_name":"Hesse","first_name":"Michael"},{"first_name":"Luis","last_name":"Schwarzer","full_name":"Schwarzer, Luis"}],"year":"2025","place":"Paderborn","citation":{"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} }","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>","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."},"publication_status":"published","edition":"1. Auflage"},{"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","volume":423,"date_created":"2024-11-07T11:43:05Z","supervisor":[{"first_name":"Julia","id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann"},{"first_name":"Ralf","last_name":"Mikut","full_name":"Mikut, Ralf"}],"author":[{"id":"43992","full_name":"Götte, Ricarda-Samantha","last_name":"Götte","first_name":"Ricarda-Samantha"}],"year":"2024","intvolume":"       423","citation":{"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>.","ieee":"R.-S. Götte, <i>Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit</i>, vol. 423. 2024.","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>","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>","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."},"publication_identifier":{"isbn":["978-3-947647-42-2"]},"publication_status":"published","keyword":["state estimation","joint estimation","sparsity"],"language":[{"iso":"ger"}],"_id":"56940","department":[{"_id":"880"},{"_id":"153"}],"series_title":"Verlagsschriftenreihe des Heinz Nixdorf Instituts","user_id":"43992","abstract":[{"lang":"ger","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. "},{"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"},{"user_id":"15402","department":[{"_id":"153"},{"_id":"880"}],"project":[{"_id":"690","name":"DART: Datengetriebene Methoden in der Regelungstechnik"}],"_id":"59051","language":[{"iso":"eng"}],"type":"journal_article","publication":"PAMM","status":"public","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>"}],"author":[{"first_name":"Ricarda-Samantha","full_name":"Götte, Ricarda-Samantha","id":"43992","last_name":"Götte"},{"first_name":"Julia","last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia"}],"date_created":"2025-03-17T07:06:12Z","volume":25,"date_updated":"2025-09-03T10:36:10Z","publisher":"Wiley","doi":"10.1002/pamm.202400080","title":"Online Learning With Joint State and Model Estimation","issue":"1","publication_status":"published","publication_identifier":{"issn":["1617-7061","1617-7061"]},"citation":{"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>.","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)."},"intvolume":"        25","year":"2024"},{"_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":[{"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.","lang":"eng"}],"status":"public","date_updated":"2024-11-13T08:43:05Z","volume":56,"date_created":"2022-12-01T07:17:00Z","author":[{"last_name":"Götte","full_name":"Götte, Ricarda-Samantha","id":"43992","first_name":"Ricarda-Samantha"},{"id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann","first_name":"Julia"}],"title":"Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF","conference":{"name":"12th IFAC Symposium on Nonlinear Control Systems NOLCOS 2022","start_date":"2023-01-04","end_date":"2023-01-06","location":"Canberra, Australien"},"doi":"https://doi.org/10.1016/j.ifacol.2023.02.015","quality_controlled":"1","issue":"1","year":"2023","intvolume":"        56","page":"85-90","citation":{"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>.","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>."}},{"department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","_id":"44326","language":[{"iso":"eng"}],"keyword":["joint estimation","unscented Kalman filter","sparsity","Laplacian prior","regularized horseshoe","principal component analysis"],"publication":"IFAC-PapersOnLine","type":"conference","status":"public","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"}],"volume":56,"author":[{"first_name":"Ricarda-Samantha","last_name":"Götte","full_name":"Götte, Ricarda-Samantha","id":"43992"},{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"}],"date_created":"2023-05-02T15:16:43Z","date_updated":"2024-11-13T08:42:37Z","conference":{"name":"22nd IFAC World Congress","start_date":"2023-07-09","end_date":"2023-07-14","location":"Yokohama, Japan"},"title":"Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF","issue":"2","quality_controlled":"1","page":"869-874","intvolume":"        56","citation":{"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.","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.","short":"R.-S. Götte, J. Timmermann, in: IFAC-PapersOnLine, 2023, pp. 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} }","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."},"year":"2023"},{"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","page":"113-123","citation":{"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>","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} }","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>"},"year":"2023","quality_controlled":"1","doi":"10.5445/KSP/1000162754","conference":{"location":"Berlin, Germany","end_date":"2023-11-24","start_date":"2023-11-23","name":"33. Workshop Computational Intelligence"},"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","full_name":"Götte, Ricarda-Samantha","id":"43992","last_name":"Götte"},{"last_name":"Klusmann","full_name":"Klusmann, Jo Noel","first_name":"Jo Noel"},{"id":"15402","full_name":"Timmermann, Julia","last_name":"Timmermann","first_name":"Julia"}],"date_created":"2023-10-26T08:11:25Z","date_updated":"2024-11-13T08:42:53Z","oa":"1"},{"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":[{"url":"https://arxiv.org/abs/2112.08148","open_access":"1"}],"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","date_updated":"2024-11-13T08:43:28Z","oa":"1","author":[{"id":"43992","full_name":"Götte, Ricarda-Samantha","last_name":"Götte","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>.","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>","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} }"},"page":"67-76","quality_controlled":"1"},{"publication":"14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)","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. "}],"language":[{"iso":"eng"}],"keyword":["neural networks","physics-guided","data-driven","multi-objective optimization","system identification","machine learning","dynamical systems"],"issue":"12","quality_controlled":"1","year":"2022","date_created":"2022-05-05T06:22:55Z","title":"Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems","type":"conference","status":"public","department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","_id":"31066","page":"19-24","intvolume":"        55","citation":{"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} }","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>","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>.","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>"},"volume":55,"author":[{"first_name":"Oliver","full_name":"Schön, Oliver","last_name":"Schön"},{"first_name":"Ricarda-Samantha","full_name":"Götte, Ricarda-Samantha","id":"43992","last_name":"Götte"},{"full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann","first_name":"Julia"}],"date_updated":"2024-11-13T08:43:16Z","doi":"https://doi.org/10.1016/j.ifacol.2022.07.282","conference":{"location":"Casablanca, Morocco","end_date":"2022-07-01","start_date":"2022-06-29","name":"14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)"}}]