[{"abstract":[{"text":"Ziel dieser Arbeit ist die Entwicklung eines modellbasierten Beobachters für eingangsaffine, nichtlineare Systeme, der trotz Modellungenauigkeiten eine hohe Schätzgüte erzielt und zusätzlich eine parametrische, physikalisch interpretierbare Darstellung dieser ermöglicht. Diese soll zur automatisierten Verbesserung des Modells verwendet werden. Die vorliegende Arbeit analysiert sowohl Techniken der hybriden Systemidentifikation wie physikalisch motivierte neuronale Netze, als auch Methoden zur Kompensation von Modellungenauigkeiten im Beobachterentwurf. Basierend auf der Analyse wird ein neuartiger, modellbasierter Beobachter entworfen, der Systemzustände und Modellungenauigkeiten gleichzeitig schätzt und insbesondere eine parametrische, physikalisch interpretierbare Darstellung der Ungenauigkeiten erzielt. Diese besteht aus einer Linearkombination von physikalisch interpretierbaren Funktionen, deren dazugehörige, dünnbesetzt modellierte Parameter mithilfe eines augmentierten Zustands parallel zu den Systemzuständen geschätzt werden. Das Novum dieser Arbeit stellt somit die echtzeitfähige Schätzung von Zuständen und Modellungenauigkeiten in physikalisch-technischer Form dar, auf deren Grundlage ein Konzept zur automatisierten Modelladaption umgesetzt wird. Die Applikation der neuartigen Methode ist in der Situation auftretender Systemveränderungen besonders vorteilhaft, da diese zur Laufzeit durch den augmentierten Beobachter\r\ngeschätzt und identifiziert werden können. ","lang":"ger"},{"text":"The aim of this thesis is the development of a model-based observer for input-affine, nonlinear systems that achieves a high estimation quality despite model inaccuracies. By additionally providing a parametric, physically interpretable representation of the model inaccuracies, an automated improvement of the model should be enabled. This thesis\r\nanalyzes techniques of hybrid system identification such as physics-guided neural networks, as well as methods for compensating model inaccuracies within the observer design. Based on this analysis, a novel model-based observer is designed, which estimates states and model inaccuracies jointly and, in particular, obtains a parametric, physically\r\ninterpretable representation of the inaccuracies. This consists of a linear combination of physically interpretable functions, whose associated parameters are modeled sparse and estimated in parallel to the system’s states using an augmented state. The novelty of this thesis is thus the real-time capability to jointly estimate states and model inaccuracies in a physical-technical manner, on the basis of which an automated model adaption can be\r\ncarried out. The application of the new methodology is particularly advantageous in the situation of occurring system changes since these can be estimated and identified at run time by the augmented observer.","lang":"eng"}],"status":"public","type":"dissertation","keyword":["state estimation","joint estimation","sparsity"],"language":[{"iso":"ger"}],"_id":"56940","series_title":"Verlagsschriftenreihe des Heinz Nixdorf Instituts","user_id":"43992","department":[{"_id":"880"},{"_id":"153"}],"year":"2024","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>","bibtex":"@book{Götte_2024, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit}, volume={423}, DOI={<a href=\"https://doi.org/10.17619/UNIPB/1-2066\">10.17619/UNIPB/1-2066</a>}, author={Götte, Ricarda-Samantha}, year={2024}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts} }","mla":"Götte, Ricarda-Samantha. <i>Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit</i>. 2024, doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2066\">10.17619/UNIPB/1-2066</a>.","short":"R.-S. Götte, Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit, 2024.","apa":"Götte, R.-S. (2024). <i>Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit</i> (Vol. 423). <a href=\"https://doi.org/10.17619/UNIPB/1-2066\">https://doi.org/10.17619/UNIPB/1-2066</a>"},"intvolume":"       423","publication_status":"published","publication_identifier":{"isbn":["978-3-947647-42-2"]},"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","author":[{"last_name":"Götte","id":"43992","full_name":"Götte, Ricarda-Samantha","first_name":"Ricarda-Samantha"}],"date_created":"2024-11-07T11:43:05Z","supervisor":[{"last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402","first_name":"Julia"},{"full_name":"Mikut, Ralf","last_name":"Mikut","first_name":"Ralf"}],"volume":423},{"issue":"1","quality_controlled":"1","intvolume":"        56","page":"85-90","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>","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} }","short":"R.-S. Götte, J. Timmermann, in: 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), 2023, pp. 85–90.","mla":"Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF.” <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>, vol. 56, no. 1, 2023, pp. 85–90, doi:<a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>.","ama":"Götte R-S, Timmermann J. Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF. In: <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>. Vol 56. ; 2023:85-90. doi:<a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>","ieee":"R.-S. Götte and J. Timmermann, “Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF,” in <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>, Canberra, Australien, 2023, vol. 56, no. 1, pp. 85–90, doi: <a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>.","chicago":"Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF.” In <i>12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)</i>, 56:85–90, 2023. <a href=\"https://doi.org/10.1016/j.ifacol.2023.02.015\">https://doi.org/10.1016/j.ifacol.2023.02.015</a>."},"year":"2023","volume":56,"date_created":"2022-12-01T07:17:00Z","author":[{"first_name":"Ricarda-Samantha","last_name":"Götte","id":"43992","full_name":"Götte, Ricarda-Samantha"},{"last_name":"Timmermann","id":"15402","full_name":"Timmermann, Julia","first_name":"Julia"}],"date_updated":"2024-11-13T08:43:05Z","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","title":"Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF","publication":"12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)","type":"conference","status":"public","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"}],"department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","_id":"34171","language":[{"iso":"eng"}],"keyword":["joint estimation","unscented transform","Kalman filter","sparsity","data-driven","compressed sensing"]},{"publication":"IFAC-PapersOnLine","type":"conference","status":"public","abstract":[{"lang":"eng","text":"Low-quality models that miss relevant dynamics lead to major challenges in modelbased\r\nstate estimation. We address this issue by simultaneously estimating the system’s states\r\nand its model inaccuracies by a square root unscented Kalman filter (SRUKF). Concretely,\r\nwe augment the state with the parameter vector of a linear combination containing suitable\r\nfunctions that approximate the lacking dynamics. Presuming that only a few dynamical terms\r\nare relevant, the parameter vector is claimed to be sparse. In Bayesian setting, properties like\r\nsparsity are expressed by a prior distribution. One common choice for sparsity is a Laplace\r\ndistribution. However, due to disadvantages of a Laplacian prior in regards to the SRUKF,\r\nthe regularized horseshoe distribution, a Gaussian that approximately features sparsity, is\r\napplied instead. Results exhibit small estimation errors with model improvements detected by\r\nan automated model reduction technique."}],"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"],"issue":"2","quality_controlled":"1","intvolume":"        56","page":"869-874","citation":{"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.","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.","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.","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.","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."},"year":"2023","volume":56,"author":[{"first_name":"Ricarda-Samantha","full_name":"Götte, Ricarda-Samantha","id":"43992","last_name":"Götte"},{"first_name":"Julia","full_name":"Timmermann, Julia","id":"15402","last_name":"Timmermann"}],"date_created":"2023-05-02T15:16:43Z","date_updated":"2024-11-13T08:42:37Z","conference":{"end_date":"2023-07-14","location":"Yokohama, Japan","name":"22nd IFAC World Congress","start_date":"2023-07-09"},"title":"Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF"}]