Safety Under State Uncertainty: Robustifying Control Barrier Functions
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911119087501312 |
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| author | Nanayakkara, Rahal Ames, Aaron D. Tabuada, Paulo |
| author_facet | Nanayakkara, Rahal Ames, Aaron D. Tabuada, Paulo |
| contents | Safety-critical control is a crucial aspect of modern systems, and Control Barrier Functions (CBFs) have gained popularity as the framework of choice for ensuring safety. However, implementing a CBF requires exact knowledge of the true state, a requirement that is often violated in real-world applications where only noisy or estimated state information is available. This paper introduces the notion of Robust Control Barrier Functions (R-CBF) for ensuring safety under such state uncertainty without requiring prior knowledge of the magnitude of uncertainty. We formally characterize the class of robustifying terms that ensure robust closed-loop safety and show how a robustly safe controller can be constructed. We demonstrate the effectiveness of this approach through simulations and compare it to existing methods, highlighting the additional robustness and convergence guarantees it provides. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17226 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safety Under State Uncertainty: Robustifying Control Barrier Functions Nanayakkara, Rahal Ames, Aaron D. Tabuada, Paulo Systems and Control Safety-critical control is a crucial aspect of modern systems, and Control Barrier Functions (CBFs) have gained popularity as the framework of choice for ensuring safety. However, implementing a CBF requires exact knowledge of the true state, a requirement that is often violated in real-world applications where only noisy or estimated state information is available. This paper introduces the notion of Robust Control Barrier Functions (R-CBF) for ensuring safety under such state uncertainty without requiring prior knowledge of the magnitude of uncertainty. We formally characterize the class of robustifying terms that ensure robust closed-loop safety and show how a robustly safe controller can be constructed. We demonstrate the effectiveness of this approach through simulations and compare it to existing methods, highlighting the additional robustness and convergence guarantees it provides. |
| title | Safety Under State Uncertainty: Robustifying Control Barrier Functions |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2508.17226 |