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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.03899 |
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| _version_ | 1866915478634496000 |
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| author | Mulagaleti, Sampath Kumar Del Prete, Andrea |
| author_facet | Mulagaleti, Sampath Kumar Del Prete, Andrea |
| contents | Control Invariant (CI) sets are instrumental in certifying the safety of dynamical systems. Control Barrier Functions (CBFs) are effective tools to compute such sets, since the zero sublevel sets of CBFs are CI sets. However, computing CBFs generally involves addressing a complex robust optimization problem, which can be intractable. Scenario-based methods have been proposed to simplify this computation. Then, one needs to verify if the CBF actually satisfies the robust constraints. We present an approach to perform this verification that relies on Lipschitz arguments, and forms the basis of a certification algorithm designed for sample efficiency. Through a numerical example, we validated the efficiency of the proposed procedure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03899 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sample Efficient Certification of Discrete-Time Control Barrier Functions Mulagaleti, Sampath Kumar Del Prete, Andrea Systems and Control Machine Learning Control Invariant (CI) sets are instrumental in certifying the safety of dynamical systems. Control Barrier Functions (CBFs) are effective tools to compute such sets, since the zero sublevel sets of CBFs are CI sets. However, computing CBFs generally involves addressing a complex robust optimization problem, which can be intractable. Scenario-based methods have been proposed to simplify this computation. Then, one needs to verify if the CBF actually satisfies the robust constraints. We present an approach to perform this verification that relies on Lipschitz arguments, and forms the basis of a certification algorithm designed for sample efficiency. Through a numerical example, we validated the efficiency of the proposed procedure. |
| title | Sample Efficient Certification of Discrete-Time Control Barrier Functions |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2509.03899 |