Sampled-data funnel control and its use for safe continual learning
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866916595948847104 |
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| author | Lanza, Lukas Dennstädt, Dario Worthmann, Karl Schmitz, Philipp Şen, Gökçen Devlet Trenn, Stephan Schaller, Manuel |
| author_facet | Lanza, Lukas Dennstädt, Dario Worthmann, Karl Schmitz, Philipp Şen, Gökçen Devlet Trenn, Stephan Schaller, Manuel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_00523 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sampled-data funnel control and its use for safe continual learning Lanza, Lukas Dennstädt, Dario Worthmann, Karl Schmitz, Philipp Şen, Gökçen Devlet Trenn, Stephan Schaller, Manuel Optimization and Control 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. |
| title | Sampled-data funnel control and its use for safe continual learning |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2303.00523 |