Sampled-data funnel control and its use for safe continual learning

We propose a novel sampled-data output-feedback controller for nonlinear systems of arbitrary relative degree that ensures reference tracking within prescribed error bounds. We provide explicit bounds on the maximum input signal and the required uniform sampling time. A key strength of this approach is its capability to serve as a safety filter for various learning-based controller designs, enabling the use of learning techniques in safety-critical applications. We illustrate its versatility by integrating it with two different controllers: a reinforcement learning controller and a non-parametric predictive controller based on Willems et al.’s fundamental lemma. Numerical simulations illustrate effectiveness of the combined controller design.

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Publication Details

Journal
Systems & Control Letters
Published
2024-08-09
DOI
https://doi.org/10.1016/j.sysconle.2024.105892
Citations
9
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
2.95

Funders

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article

Sampled-data funnel control and its use for safe continual learning

Manuel Schaller, Lukas Lanza, Karl Worthmann, Gökçen Devlet Şen et al.
9 citations
Systems & Control Letters
Fault Detection and Control Systems
2.95
article

Sampled-data funnel control and its use for safe continual learning

Manuel Schaller, Lukas Lanza, Karl Worthmann, Gökçen Devlet Şen, Stephan Trenn, Dario Dennstädt, Philipp Schmitz
article en
9 citations

Abstract

We propose a novel sampled-data output-feedback controller for nonlinear systems of arbitrary relative degree that ensures reference tracking within prescribed error bounds. We provide explicit bounds on the maximum input signal and the required uniform sampling time. A key strength of this approach is its capability to serve as a safety filter for various learning-based controller designs, enabling the use of learning techniques in safety-critical applications. We illustrate its versatility by integrating it with two different controllers: a reinforcement learning controller and a non-parametric predictive controller based on Willems et al.’s fundamental lemma. Numerical simulations illustrate effectiveness of the combined controller design.

Systems & Control LettersVol. 192
Technische Universität Ilmenau (DE), University of Groningen (NL), Paderborn University (DE), Istanbul Technical University (TR), Brandenburg University of Technology Cottbus-Senftenberg (DE)
Deutscher Akademischer Austauschdienst, Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 9%
Fault Detection and Control Systems
2.95
DOIOpen Access PDFOpenAlex
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Sampled-data funnel control and its use for safe continual learning — Manuel Schaller, Lukas Lanza, et al. · Systems & Control Letters (2024) | TGRS Research Map | TGRS