Online Learning-Enhanced High Order Adaptive Safety Control
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911585991131136 |
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| author | Pan, Lishuo Catellani, Mattia Silva, Thales C. Sabattini, Lorenzo Ayanian, Nora |
| author_facet | Pan, Lishuo Catellani, Mattia Silva, Thales C. Sabattini, Lorenzo Ayanian, Nora |
| contents | Control barrier functions (CBFs) are an effective model-based tool to formally certify the safety of a system. With the growing complexity of modern control problems, CBFs have received increasing attention in both optimization-based and learning-based control communities as a safety filter, owing to their provable guarantees. However, success in transferring these guarantees to real-world systems is critically tied to model accuracy. For example, payloads or wind disturbances can significantly influence the dynamics of an aerial vehicle and invalidate the safety guarantee. In this work, we propose an efficient yet flexible online learning-enhanced high-order adaptive control barrier function using Neural ODEs. Our approach improves the safety of a CBF controller on the fly, even under complex time-varying model perturbations. In particular, we deploy our hybrid adaptive CBF controller on a 38g nano quadrotor, keeping a safe distance from the obstacle, against 18km/h wind. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19651 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Learning-Enhanced High Order Adaptive Safety Control Pan, Lishuo Catellani, Mattia Silva, Thales C. Sabattini, Lorenzo Ayanian, Nora Robotics Control barrier functions (CBFs) are an effective model-based tool to formally certify the safety of a system. With the growing complexity of modern control problems, CBFs have received increasing attention in both optimization-based and learning-based control communities as a safety filter, owing to their provable guarantees. However, success in transferring these guarantees to real-world systems is critically tied to model accuracy. For example, payloads or wind disturbances can significantly influence the dynamics of an aerial vehicle and invalidate the safety guarantee. In this work, we propose an efficient yet flexible online learning-enhanced high-order adaptive control barrier function using Neural ODEs. Our approach improves the safety of a CBF controller on the fly, even under complex time-varying model perturbations. In particular, we deploy our hybrid adaptive CBF controller on a 38g nano quadrotor, keeping a safe distance from the obstacle, against 18km/h wind. |
| title | Online Learning-Enhanced High Order Adaptive Safety Control |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.19651 |