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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2505.06845 |
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| _version_ | 1866916731688058880 |
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| author | Tan, Xiao Sundar, Junior Bruzzone, Renzo Ong, Pio Lunardi, Willian T. Andreoni, Martin Tabuada, Paulo Ames, Aaron D. |
| author_facet | Tan, Xiao Sundar, Junior Bruzzone, Renzo Ong, Pio Lunardi, Willian T. Andreoni, Martin Tabuada, Paulo Ames, Aaron D. |
| contents | Modern autopilot systems are prone to sensor attacks that can jeopardize flight safety. To mitigate this risk, we proposed a modular solution: the secure safety filter, which extends the well-established control barrier function (CBF)-based safety filter to account for, and mitigate, sensor attacks. This module consists of a secure state reconstructor (which generates plausible states) and a safety filter (which computes the safe control input that is closest to the nominal one). Differing from existing work focusing on linear, noise-free systems, the proposed secure safety filter handles bounded measurement noise and, by leveraging reduced-order model techniques, is applicable to the nonlinear dynamics of drones. Software-in-the-loop simulations and drone hardware experiments demonstrate the effectiveness of the secure safety filter in rendering the system safe in the presence of sensor attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06845 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Secure Safety Filter: Towards Safe Flight Control under Sensor Attacks Tan, Xiao Sundar, Junior Bruzzone, Renzo Ong, Pio Lunardi, Willian T. Andreoni, Martin Tabuada, Paulo Ames, Aaron D. Robotics Systems and Control Modern autopilot systems are prone to sensor attacks that can jeopardize flight safety. To mitigate this risk, we proposed a modular solution: the secure safety filter, which extends the well-established control barrier function (CBF)-based safety filter to account for, and mitigate, sensor attacks. This module consists of a secure state reconstructor (which generates plausible states) and a safety filter (which computes the safe control input that is closest to the nominal one). Differing from existing work focusing on linear, noise-free systems, the proposed secure safety filter handles bounded measurement noise and, by leveraging reduced-order model techniques, is applicable to the nonlinear dynamics of drones. Software-in-the-loop simulations and drone hardware experiments demonstrate the effectiveness of the secure safety filter in rendering the system safe in the presence of sensor attacks. |
| title | Secure Safety Filter: Towards Safe Flight Control under Sensor Attacks |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2505.06845 |