---
_id: '61118'
abstract:
- lang: eng
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.
author:
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Annika
full_name: Junker, Annika
id: '41470'
last_name: Junker
orcid: 0009-0002-6475-2503
- first_name: Michael
full_name: Hesse, Michael
id: '29222'
last_name: Hesse
- first_name: Luis
full_name: Schwarzer, Luis
last_name: Schwarzer
citation:
ama: Timmermann J, Götte R-S, Junker A, Hesse M, Schwarzer L. DART - Datengetriebene
Methoden in der Regelungstechnik. Vol Band 430. 1. Auflage. HNI Verlagsschriftenreihe;
2025. doi:10.17619/UNIPB/1-2305
apa: 'Timmermann, J., Götte, R.-S., Junker, A., Hesse, M., & Schwarzer, L. (2025).
DART - Datengetriebene Methoden in der Regelungstechnik: Vol. Band 430
(1. Auflage). HNI Verlagsschriftenreihe. https://doi.org/10.17619/UNIPB/1-2305'
bibtex: '@book{Timmermann_Götte_Junker_Hesse_Schwarzer_2025, place={Paderborn},
edition={1. Auflage}, title={DART - Datengetriebene Methoden in der Regelungstechnik},
volume={Band 430}, DOI={10.17619/UNIPB/1-2305},
publisher={HNI Verlagsschriftenreihe}, author={Timmermann, Julia and Götte, Ricarda-Samantha
and Junker, Annika and Hesse, Michael and Schwarzer, Luis}, year={2025} }'
chicago: 'Timmermann, Julia, Ricarda-Samantha Götte, Annika Junker, Michael Hesse,
and Luis Schwarzer. DART - Datengetriebene Methoden in der Regelungstechnik.
1. Auflage. Vol. Band 430. Paderborn: HNI Verlagsschriftenreihe, 2025. https://doi.org/10.17619/UNIPB/1-2305.'
ieee: 'J. Timmermann, R.-S. Götte, A. Junker, M. Hesse, and L. Schwarzer, DART
- Datengetriebene Methoden in der Regelungstechnik, 1. Auflage., vol. Band
430. Paderborn: HNI Verlagsschriftenreihe, 2025.'
mla: Timmermann, Julia, et al. DART - Datengetriebene Methoden in der Regelungstechnik.
1. Auflage, vol. Band 430, HNI Verlagsschriftenreihe, 2025, doi:10.17619/UNIPB/1-2305.
short: J. Timmermann, R.-S. Götte, A. Junker, M. Hesse, L. Schwarzer, DART - Datengetriebene
Methoden in der Regelungstechnik, 1. Auflage, HNI Verlagsschriftenreihe, Paderborn,
2025.
date_created: 2025-09-03T09:35:35Z
date_updated: 2026-04-01T06:14:00Z
department:
- _id: '880'
- _id: '153'
doi: 10.17619/UNIPB/1-2305
edition: 1. Auflage
language:
- iso: ger
main_file_link:
- open_access: '1'
url: https://digital.ub.uni-paderborn.de/doi/10.17619/UNIPB/1-2305
oa: '1'
place: Paderborn
publication_status: published
publisher: HNI Verlagsschriftenreihe
status: public
title: DART - Datengetriebene Methoden in der Regelungstechnik
type: book
user_id: '41470'
volume: Band 430
year: '2025'
...
---
_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. Online-Schätzung von Modellungenauigkeiten zur automatischen
Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit.
Vol 423.; 2024. doi:10.17619/UNIPB/1-2066
apa: Götte, R.-S. (2024). Online-Schätzung von Modellungenauigkeiten zur automatischen
Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit
(Vol. 423). https://doi.org/10.17619/UNIPB/1-2066
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={10.17619/UNIPB/1-2066},
author={Götte, Ricarda-Samantha}, year={2024}, collection={Verlagsschriftenreihe
des Heinz Nixdorf Instituts} }'
chicago: Götte, Ricarda-Samantha. Online-Schätzung von Modellungenauigkeiten
zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen
Interpretierbarkeit. Vol. 423. Verlagsschriftenreihe des Heinz Nixdorf Instituts,
2024. https://doi.org/10.17619/UNIPB/1-2066.
ieee: R.-S. Götte, Online-Schätzung von Modellungenauigkeiten zur automatischen
Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit,
vol. 423. 2024.
mla: Götte, Ricarda-Samantha. Online-Schätzung von Modellungenauigkeiten zur
automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen
Interpretierbarkeit. 2024, doi:10.17619/UNIPB/1-2066.
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: '59051'
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.'
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. Online Learning With Joint State and Model Estimation.
PAMM. 2024;25(1). doi:10.1002/pamm.202400080
apa: Götte, R.-S., & Timmermann, J. (2024). Online Learning With Joint State
and Model Estimation. PAMM, 25(1). https://doi.org/10.1002/pamm.202400080
bibtex: '@article{Götte_Timmermann_2024, title={Online Learning With Joint State
and Model Estimation}, volume={25}, DOI={10.1002/pamm.202400080},
number={1}, journal={PAMM}, publisher={Wiley}, author={Götte, Ricarda-Samantha
and Timmermann, Julia}, year={2024} }'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Online Learning With Joint
State and Model Estimation.” PAMM 25, no. 1 (2024). https://doi.org/10.1002/pamm.202400080.
ieee: 'R.-S. Götte and J. Timmermann, “Online Learning With Joint State and Model
Estimation,” PAMM, vol. 25, no. 1, 2024, doi: 10.1002/pamm.202400080.'
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Online Learning With Joint
State and Model Estimation.” PAMM, vol. 25, no. 1, Wiley, 2024, doi:10.1002/pamm.202400080.
short: R.-S. Götte, J. Timmermann, PAMM 25 (2024).
date_created: 2025-03-17T07:06:12Z
date_updated: 2025-09-03T10:36:10Z
department:
- _id: '153'
- _id: '880'
doi: 10.1002/pamm.202400080
intvolume: ' 25'
issue: '1'
language:
- iso: eng
project:
- _id: '690'
name: 'DART: Datengetriebene Methoden in der Regelungstechnik'
publication: PAMM
publication_identifier:
issn:
- 1617-7061
- 1617-7061
publication_status: published
publisher: Wiley
status: public
title: Online Learning With Joint State and Model Estimation
type: journal_article
user_id: '15402'
volume: 25
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: 12th IFAC Symposium on Nonlinear Control Systems
(NOLCOS 2022). Vol 56. ; 2023:85-90. doi:https://doi.org/10.1016/j.ifacol.2023.02.015'
apa: Götte, R.-S., & Timmermann, J. (2023). Estimating States and Model Uncertainties
Jointly by a Sparsity Promoting UKF. 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), 56(1), 85–90. https://doi.org/10.1016/j.ifacol.2023.02.015
bibtex: '@inproceedings{Götte_Timmermann_2023, title={Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF}, volume={56}, DOI={https://doi.org/10.1016/j.ifacol.2023.02.015},
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 12th IFAC Symposium
on Nonlinear Control Systems (NOLCOS 2022), 56:85–90, 2023. https://doi.org/10.1016/j.ifacol.2023.02.015.
ieee: 'R.-S. Götte and J. Timmermann, “Estimating States and Model Uncertainties
Jointly by a Sparsity Promoting UKF,” in 12th IFAC Symposium on Nonlinear Control
Systems (NOLCOS 2022), Canberra, Australien, 2023, vol. 56, no. 1, pp. 85–90,
doi: https://doi.org/10.1016/j.ifacol.2023.02.015.'
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Estimating States and Model
Uncertainties Jointly by a Sparsity Promoting UKF.” 12th IFAC Symposium on
Nonlinear Control Systems (NOLCOS 2022), vol. 56, no. 1, 2023, pp. 85–90,
doi:https://doi.org/10.1016/j.ifacol.2023.02.015.
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: IFAC-PapersOnLine. Vol 56. ; 2023:869-874.'
apa: Götte, R.-S., & Timmermann, J. (2023). Approximating a Laplacian Prior
for Joint State and Model Estimation within an UKF. IFAC-PapersOnLine,
56(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 IFAC-PapersOnLine,
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 IFAC-PapersOnLine, 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.” IFAC-PapersOnLine,
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'
...
---
_id: '48482'
author:
- first_name: Ricarda-Samantha
full_name: Götte, Ricarda-Samantha
id: '43992'
last_name: Götte
- first_name: Jo Noel
full_name: Klusmann, Jo Noel
last_name: Klusmann
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: 'Götte R-S, Klusmann JN, Timmermann J. Data-driven identification of disturbances
using a sliding mode observer. In: Proceedings - 33. Workshop Computational
Intelligence: Berlin, 23.-24. November 2023. ; 2023:113-123. doi:10.5445/KSP/1000162754'
apa: 'Götte, R.-S., Klusmann, J. N., & Timmermann, J. (2023). Data-driven identification
of disturbances using a sliding mode observer. Proceedings - 33. Workshop Computational
Intelligence: Berlin, 23.-24. November 2023, 113–123. https://doi.org/10.5445/KSP/1000162754'
bibtex: '@inproceedings{Götte_Klusmann_Timmermann_2023, title={Data-driven identification
of disturbances using a sliding mode observer}, DOI={10.5445/KSP/1000162754},
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} }'
chicago: 'Götte, Ricarda-Samantha, Jo Noel Klusmann, and Julia Timmermann. “Data-Driven
Identification of Disturbances Using a Sliding Mode Observer.” In Proceedings
- 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023,
113–23, 2023. https://doi.org/10.5445/KSP/1000162754.'
ieee: 'R.-S. Götte, J. N. Klusmann, and J. Timmermann, “Data-driven identification
of disturbances using a sliding mode observer,” in Proceedings - 33. Workshop
Computational Intelligence: Berlin, 23.-24. November 2023, Berlin, Germany,
2023, pp. 113–123, doi: 10.5445/KSP/1000162754.'
mla: 'Götte, Ricarda-Samantha, et al. “Data-Driven Identification of Disturbances
Using a Sliding Mode Observer.” Proceedings - 33. Workshop Computational Intelligence:
Berlin, 23.-24. November 2023, 2023, pp. 113–23, doi:10.5445/KSP/1000162754.'
short: 'R.-S. Götte, J.N. Klusmann, J. Timmermann, in: Proceedings - 33. Workshop
Computational Intelligence: Berlin, 23.-24. November 2023, 2023, pp. 113–123.'
conference:
end_date: 2023-11-24
location: Berlin, Germany
name: 33. Workshop Computational Intelligence
start_date: 2023-11-23
date_created: 2023-10-26T08:11:25Z
date_updated: 2024-11-13T08:42:53Z
department:
- _id: '153'
- _id: '880'
doi: 10.5445/KSP/1000162754
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ksp.kit.edu/site/books/e/10.5445/KSP/1000162754/
oa: '1'
page: 113-123
publication: 'Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24.
November 2023'
quality_controlled: '1'
status: public
title: Data-driven identification of disturbances using a sliding mode observer
type: conference
user_id: '43992'
year: '2023'
...
---
_id: '26539'
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.
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. Composed Physics- and Data-driven System Identification
for Non-autonomous Systems in Control Engineering. In: 2022 3rd International
Conference on Artificial Intelligence, Robotics and Control (AIRC). ; 2022:67-76.
doi:10.1109/AIRC56195.2022.9836982'
apa: Götte, R.-S., & Timmermann, J. (2022). Composed Physics- and Data-driven
System Identification for Non-autonomous Systems in Control Engineering. 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 67–76. https://doi.org/10.1109/AIRC56195.2022.9836982
bibtex: '@inproceedings{Götte_Timmermann_2022, title={Composed Physics- and Data-driven
System Identification for Non-autonomous Systems in Control Engineering}, DOI={10.1109/AIRC56195.2022.9836982},
booktitle={2022 3rd International Conference on Artificial Intelligence, Robotics
and Control (AIRC)}, author={Götte, Ricarda-Samantha and Timmermann, Julia}, year={2022},
pages={67–76} }'
chicago: Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven
System Identification for Non-Autonomous Systems in Control Engineering.” In 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 67–76, 2022. https://doi.org/10.1109/AIRC56195.2022.9836982.
ieee: 'R.-S. Götte and J. Timmermann, “Composed Physics- and Data-driven System
Identification for Non-autonomous Systems in Control Engineering,” in 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), Cairo, Egypt, 2022, pp. 67–76, doi: 10.1109/AIRC56195.2022.9836982.'
mla: Götte, Ricarda-Samantha, and Julia Timmermann. “Composed Physics- and Data-Driven
System Identification for Non-Autonomous Systems in Control Engineering.” 2022
3rd International Conference on Artificial Intelligence, Robotics and Control
(AIRC), 2022, pp. 67–76, doi:10.1109/AIRC56195.2022.9836982.
short: 'R.-S. Götte, J. Timmermann, in: 2022 3rd International Conference on Artificial
Intelligence, Robotics and Control (AIRC), 2022, pp. 67–76.'
conference:
end_date: 2021-12-10
location: Cairo, Egypt
name: 3rd International Conference on Artificial Intelligence, Robotics and Control
start_date: 2021-12-08
date_created: 2021-10-19T14:47:17Z
date_updated: 2024-11-13T08:43:28Z
department:
- _id: '153'
- _id: '880'
doi: 10.1109/AIRC56195.2022.9836982
keyword:
- data-driven
- physics-based
- physics-informed
- neural networks
- system identification
- hybrid modelling
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2112.08148
oa: '1'
page: 67-76
publication: 2022 3rd International Conference on Artificial Intelligence, Robotics
and Control (AIRC)
quality_controlled: '1'
status: public
title: Composed Physics- and Data-driven System Identification for Non-autonomous
Systems in Control Engineering
type: conference
user_id: '43992'
year: '2022'
...
---
_id: '31066'
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. '
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
- first_name: Julia
full_name: Timmermann, Julia
id: '15402'
last_name: Timmermann
citation:
ama: 'Schön O, Götte R-S, Timmermann J. Multi-Objective Physics-Guided Recurrent
Neural Networks for Identifying Non-Autonomous Dynamical Systems. In: 14th
IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022). Vol 55.
; 2022:19-24. doi:https://doi.org/10.1016/j.ifacol.2022.07.282'
apa: Schön, O., Götte, R.-S., & Timmermann, J. (2022). Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. 14th
IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 55(12),
19–24. https://doi.org/10.1016/j.ifacol.2022.07.282
bibtex: '@inproceedings{Schön_Götte_Timmermann_2022, title={Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}, volume={55},
DOI={https://doi.org/10.1016/j.ifacol.2022.07.282},
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} }'
chicago: Schön, Oliver, Ricarda-Samantha Götte, and Julia Timmermann. “Multi-Objective
Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical
Systems.” In 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS
2022), 55:19–24, 2022. https://doi.org/10.1016/j.ifacol.2022.07.282.
ieee: 'O. Schön, R.-S. Götte, and J. Timmermann, “Multi-Objective Physics-Guided
Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems,” in
14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022),
Casablanca, Morocco, 2022, vol. 55, no. 12, pp. 19–24, doi: https://doi.org/10.1016/j.ifacol.2022.07.282.'
mla: Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks
for Identifying Non-Autonomous Dynamical Systems.” 14th IFAC Workshop on Adaptive
and Learning Control Systems (ALCOS 2022), vol. 55, no. 12, 2022, pp. 19–24,
doi:https://doi.org/10.1016/j.ifacol.2022.07.282.
short: 'O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive
and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.'
conference:
end_date: 2022-07-01
location: Casablanca, Morocco
name: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)
start_date: 2022-06-29
date_created: 2022-05-05T06:22:55Z
date_updated: 2024-11-13T08:43:16Z
department:
- _id: '153'
- _id: '880'
doi: https://doi.org/10.1016/j.ifacol.2022.07.282
intvolume: ' 55'
issue: '12'
keyword:
- neural networks
- physics-guided
- data-driven
- multi-objective optimization
- system identification
- machine learning
- dynamical systems
language:
- iso: eng
page: 19-24
publication: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)
quality_controlled: '1'
status: public
title: Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous
Dynamical Systems
type: conference
user_id: '43992'
volume: 55
year: '2022'
...