Uncertainty Estimators for Robust Backup Control Barrier Functions
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911250750898176 |
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| author | van Wijk, David E. J. Das, Ersin Alan, Anil Coogan, Samuel Molnar, Tamas G. Burdick, Joel W. Majji, Manoranjan Hobbs, Kerianne L. |
| author_facet | van Wijk, David E. J. Das, Ersin Alan, Anil Coogan, Samuel Molnar, Tamas G. Burdick, Joel W. Majji, Manoranjan Hobbs, Kerianne L. |
| contents | Designing safe controllers is crucial and notoriously challenging for input-constrained safety-critical control systems. Backup control barrier functions offer an approach for the construction of safe controllers online by considering the flow of the system under a backup controller. However, in the presence of model uncertainties, the flow cannot be accurately computed, making this method insufficient for safety assurance. To tackle this shortcoming, we integrate backup control barrier functions with uncertainty estimators and calculate the flow under a reconstruction of the model uncertainty while refining this estimate over time. We prove that the controllers resulting from the proposed Uncertainty Estimator Backup Control Barrier Function (UE-bCBF) approach guarantee safety, are robust to unknown disturbances, and satisfy input constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15734 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncertainty Estimators for Robust Backup Control Barrier Functions van Wijk, David E. J. Das, Ersin Alan, Anil Coogan, Samuel Molnar, Tamas G. Burdick, Joel W. Majji, Manoranjan Hobbs, Kerianne L. Systems and Control Designing safe controllers is crucial and notoriously challenging for input-constrained safety-critical control systems. Backup control barrier functions offer an approach for the construction of safe controllers online by considering the flow of the system under a backup controller. However, in the presence of model uncertainties, the flow cannot be accurately computed, making this method insufficient for safety assurance. To tackle this shortcoming, we integrate backup control barrier functions with uncertainty estimators and calculate the flow under a reconstruction of the model uncertainty while refining this estimate over time. We prove that the controllers resulting from the proposed Uncertainty Estimator Backup Control Barrier Function (UE-bCBF) approach guarantee safety, are robust to unknown disturbances, and satisfy input constraints. |
| title | Uncertainty Estimators for Robust Backup Control Barrier Functions |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2503.15734 |