Learning Robust Safety Controllers for Uncertain Input-Affine Polynomial Systems
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912491736399872 |
|---|---|
| author | Akbarzadeh, Omid Ashoori, MohammadHossein Lavaei, Abolfazl |
| author_facet | Akbarzadeh, Omid Ashoori, MohammadHossein Lavaei, Abolfazl |
| contents | This paper offers a direct data-driven approach for learning robust control barrier certificates (R-CBCs) and robust safety controllers (R-SCs) for discrete-time input-affine polynomial systems with unknown dynamics under unknown-but-bounded disturbances. The proposed method relies on data from input-state observations collected over a finite-time horizon while satisfying a specific rank condition to ensure the system is persistently excited. Our data-driven scheme enables the synthesis of R-CBCs and R-SCs directly from observed data, bypassing the need for explicit modeling of the system's dynamics and thus ensuring robust system safety against disturbances within an infinite time horizon. Our proposed approach is formulated as a sum-of-squares (SOS) optimization problem, providing a structured design framework. Two case studies showcase our method's capability to provide robust safety guarantees for unknown input-affine polynomial systems under bounded disturbances, demonstrating its practical effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03919 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Robust Safety Controllers for Uncertain Input-Affine Polynomial Systems Akbarzadeh, Omid Ashoori, MohammadHossein Lavaei, Abolfazl Systems and Control This paper offers a direct data-driven approach for learning robust control barrier certificates (R-CBCs) and robust safety controllers (R-SCs) for discrete-time input-affine polynomial systems with unknown dynamics under unknown-but-bounded disturbances. The proposed method relies on data from input-state observations collected over a finite-time horizon while satisfying a specific rank condition to ensure the system is persistently excited. Our data-driven scheme enables the synthesis of R-CBCs and R-SCs directly from observed data, bypassing the need for explicit modeling of the system's dynamics and thus ensuring robust system safety against disturbances within an infinite time horizon. Our proposed approach is formulated as a sum-of-squares (SOS) optimization problem, providing a structured design framework. Two case studies showcase our method's capability to provide robust safety guarantees for unknown input-affine polynomial systems under bounded disturbances, demonstrating its practical effectiveness. |
| title | Learning Robust Safety Controllers for Uncertain Input-Affine Polynomial Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2412.03919 |