---
_id: '56940'
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. "
- lang: eng
  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."
author:
- first_name: Ricarda-Samantha
  full_name: Götte, Ricarda-Samantha
  id: '43992'
  last_name: Götte
citation:
  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} }'
  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.
  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.
date_created: 2024-11-07T11:43:05Z
date_updated: 2024-11-07T11:47:59Z
department:
- _id: '880'
- _id: '153'
doi: 10.17619/UNIPB/1-2066
intvolume: '       423'
keyword:
- state estimation
- joint estimation
- sparsity
language:
- iso: ger
publication_identifier:
  isbn:
  - 978-3-947647-42-2
publication_status: published
series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts
status: public
supervisor:
- first_name: Julia
  full_name: Timmermann, Julia
  id: '15402'
  last_name: Timmermann
- first_name: Ralf
  full_name: Mikut, Ralf
  last_name: Mikut
title: Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption
  unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit
type: dissertation
user_id: '43992'
volume: 423
year: '2024'
...
---
_id: '34171'
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.
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
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>'
  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}
    }'
  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>.
  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>.'
  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>.
  short: 'R.-S. Götte, J. Timmermann, in: 12th IFAC Symposium on Nonlinear Control
    Systems (NOLCOS 2022), 2023, pp. 85–90.'
conference:
  end_date: 2023-01-06
  location: Canberra, Australien
  name: 12th IFAC Symposium on Nonlinear Control Systems NOLCOS 2022
  start_date: 2023-01-04
date_created: 2022-12-01T07:17:00Z
date_updated: 2024-11-13T08:43:05Z
department:
- _id: '153'
- _id: '880'
doi: https://doi.org/10.1016/j.ifacol.2023.02.015
intvolume: '        56'
issue: '1'
keyword:
- joint estimation
- unscented transform
- Kalman filter
- sparsity
- data-driven
- compressed sensing
language:
- iso: eng
page: 85-90
publication: 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)
quality_controlled: '1'
status: public
title: Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF
type: conference
user_id: '43992'
volume: 56
year: '2023'
...
---
_id: '44326'
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."
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
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.'
  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} }'
  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.
  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.
  short: 'R.-S. Götte, J. Timmermann, in: IFAC-PapersOnLine, 2023, pp. 869–874.'
conference:
  end_date: 2023-07-14
  location: Yokohama, Japan
  name: 22nd IFAC World Congress
  start_date: 2023-07-09
date_created: 2023-05-02T15:16:43Z
date_updated: 2024-11-13T08:42:37Z
department:
- _id: '153'
- _id: '880'
intvolume: '        56'
issue: '2'
keyword:
- joint estimation
- unscented Kalman filter
- sparsity
- Laplacian prior
- regularized horseshoe
- principal component analysis
language:
- iso: eng
page: 869-874
publication: IFAC-PapersOnLine
quality_controlled: '1'
status: public
title: Approximating a Laplacian Prior for Joint State and Model Estimation within
  an UKF
type: conference
user_id: '43992'
volume: 56
year: '2023'
...