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On using the output progression in unscented Kalman filtering

  • Florian Meiners

    Florian Meiners received his B.S. and M.S. degrees from TU Darmstadt and his M.S. degree from the University of Rhode Island in 2018 and 2020, respectively. He is currently part of the Control Methods and Intelligent Systems Laboratory at TU Darmstadt under the supervision of Prof. Jürgen Adamy.

     EMAIL logo     and Jürgen Adamy

    Jürgen Adamy received his Diploma and Dr.-Ing. degrees in Electrical Engineering from TU Dortmund, Germany, in 1987 and 1991, respectively. From 1992 to 1998, he was an Engineer and Manager in the area of Control Applications at Siemens AG, Erlangen, Germany. In 1998, he became a Full Professor at TU Darmstadt, Germany, where he is the head of the Control Methods and Intelligent Systems Laboratory. His research interests are in the areas of nonlinear control, fuzzy systems, and mobile robots.
Published/Copyright: September 4, 2025
at - Automatisierungstechnik
From the journal at - Automatisierungstechnik Volume 73 Issue 9

Abstract

By calculating the state from past output samples, Newton observers (NO) provide an intuitive approach to state estimation in nonlinear systems. However, due to their sensitivity to measurement noise, they failed to become established. Unscented Kalman filters (UKF), on the other hand, have proven to provide good noise attenuation and perform well if the underlying system is highly nonlinear. These benefits, however, come at the cost of a, sometimes long, settling time. We propose a modified filtering algorithm that builds upon both the unscented transformation used in UKF and the rationale of the NO. By leveraging the information contained in the output progression, the new filter obtains the immediacy of the NO and improved steady-state noise suppression. State estimation with the proposed algorithm is investigated both in a large-scale simulation study and in experiments with a physical gantry crane model.

Zusammenfassung

Durch die Berechnung des Zustands aus in der Vergangenheit liegenden Ausgangswerten bieten Newton-Beobachter einen intuitiven Ansatz für die Zustandsschätzung in nichtlinearen Systemen. Aufgrund ihrer Empfindlichkeit gegenüber Messrauschen konnten sie sich jedoch nicht durchsetzen. Unscented Kalman-Filter hingegen weisen gute Rauschunterdrückung auf und zeigen gute Performanz wenn das zugrunde liegende System hochgradig nichtlinear ist. Diese Vorteile gehen jedoch mit einer, bisweilen langen, Einschwingzeit einher. Wir stellen einen modifizierten Filteralgorithmus vor, der sowohl auf der in Unscented Kalman-Filtern verwendeten unscented transformation als auch auf dem Prinzip des Newton-Beobachters basiert. Durch die Nutzung der aus dem Ausgangsverlaufs gewonnenen Informationen erhält das neue Filter die Unmittelbarkeit des Newton-Beobachters und eine verbesserte Unterdrückung von Messrauschen im eingeschwungenen Zustand. Die Zustandsschätzung mit dem vorgeschlagenen Algorithmus wird sowohl in einer groß angelegten Simulationsstudie als auch in Experimenten mit einem physikalischen Portalkranmodell untersucht.

Keywords: Kalman filtering; nonlinear state estimation; Newton observer
Schlagwörter: Kalman-Filterung; Nichtlineare Zustandsschätzung; Newton-Beobachter
Corresponding author: Florian Meiners, Control Methods and Intelligent Systems Lab, TU Darmstadt, Landgraf-Georg Str. 4, 64283 Darmstadt, Germany, E-mail: 

About the authors

Florian Meiners

Florian Meiners received his B.S. and M.S. degrees from TU Darmstadt and his M.S. degree from the University of Rhode Island in 2018 and 2020, respectively. He is currently part of the Control Methods and Intelligent Systems Laboratory at TU Darmstadt under the supervision of Prof. Jürgen Adamy.

Jürgen Adamy

Jürgen Adamy received his Diploma and Dr.-Ing. degrees in Electrical Engineering from TU Dortmund, Germany, in 1987 and 1991, respectively. From 1992 to 1998, he was an Engineer and Manager in the area of Control Applications at Siemens AG, Erlangen, Germany. In 1998, he became a Full Professor at TU Darmstadt, Germany, where he is the head of the Control Methods and Intelligent Systems Laboratory. His research interests are in the areas of nonlinear control, fuzzy systems, and mobile robots.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. FM is responsible for the presented methods and their development. FM has conducted all experiments and performed the analysis. FM is the primary author of the manuscript. JA acted as a supervisor and performed editorial tasks.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-03-04
Accepted: 2025-07-21
Published Online: 2025-09-04
Published in Print: 2025-09-25

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

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  2. Methods
  3. Flachheitsanalyse nichtlinearer Systeme aus algebraischer Perspektive
  4. On using the output progression in unscented Kalman filtering
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https://doi.org/10.1515/auto-2025-0026
Keywords for this article
Kalman filtering; nonlinear state estimation; Newton observer

Articles in the same Issue

  1. Frontmatter
  2. Methods
  3. Flachheitsanalyse nichtlinearer Systeme aus algebraischer Perspektive
  4. On using the output progression in unscented Kalman filtering
  5. Methodology for optimizing convolutional neural networks for fast production processes
  6. Skill-based multi-agent control for safe and effective human-robot collaboration
  7. Applications
  8. Advancements in intelligent wheelchairs: a scoping review
  9. Dissertations
  10. Industrie 4.0-angepasste Datenverkehrskonzepte für industrielle 5G-TSN-Netzwerke
  11. Modelling and Management of Wireless Communication Systems based on Digital Twins
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