Secure Safety Filter Design for Sampled-data Nonlinear Systems under Sensor Spoofing Attacks
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912369953734656 |
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| author | Tan, Xiao Ong, Pio Tabuada, Paulo Ames, Aaron D. |
| author_facet | Tan, Xiao Ong, Pio Tabuada, Paulo Ames, Aaron D. |
| contents | This paper presents a secure safety filter design for nonlinear systems under sensor spoofing attacks. Existing approaches primarily focus on linear systems which limits their applications in real-world scenarios. In this work, we extend these results to nonlinear systems in a principled way. We introduce exact observability maps that abstract specific state estimation algorithms and extend them to a secure version capable of handling sensor attacks. Our generalization also applies to the relaxed observability case, with slightly relaxed guarantees. More importantly, we propose a secure safety filter design in both exact and relaxed cases, which incorporates secure state estimation and a control barrier function-enabled safety filter. The proposed approach provides theoretical safety guarantees for nonlinear systems in the presence of sensor attacks. We numerically validate our analysis on a unicycle vehicle equipped with redundant yet partly compromised sensors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06842 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Secure Safety Filter Design for Sampled-data Nonlinear Systems under Sensor Spoofing Attacks Tan, Xiao Ong, Pio Tabuada, Paulo Ames, Aaron D. Systems and Control This paper presents a secure safety filter design for nonlinear systems under sensor spoofing attacks. Existing approaches primarily focus on linear systems which limits their applications in real-world scenarios. In this work, we extend these results to nonlinear systems in a principled way. We introduce exact observability maps that abstract specific state estimation algorithms and extend them to a secure version capable of handling sensor attacks. Our generalization also applies to the relaxed observability case, with slightly relaxed guarantees. More importantly, we propose a secure safety filter design in both exact and relaxed cases, which incorporates secure state estimation and a control barrier function-enabled safety filter. The proposed approach provides theoretical safety guarantees for nonlinear systems in the presence of sensor attacks. We numerically validate our analysis on a unicycle vehicle equipped with redundant yet partly compromised sensors. |
| title | Secure Safety Filter Design for Sampled-data Nonlinear Systems under Sensor Spoofing Attacks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2505.06842 |