Bayesian Formal Synthesis of Unknown Systems via Robust Simulation Relations
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915008969965568 |
|---|---|
| author | Schön, Oliver van Huijgevoort, Birgit Haesaert, Sofie Soudjani, Sadegh |
| author_facet | Schön, Oliver van Huijgevoort, Birgit Haesaert, Sofie Soudjani, Sadegh |
| contents | This paper addresses the problem of data-driven computation of controllers that are correct by design for safety-critical systems and can provably satisfy (complex) functional requirements. With a focus on continuous-space stochastic systems with parametric uncertainty, we propose a two-stage approach that decomposes the problem into a learning stage and a robust formal controller synthesis stage. The first stage utilizes available Bayesian regression results to compute robust credible sets for the true parameters of the system. For the second stage, we introduce methods for systems subject to both stochastic and parametric uncertainties. We provide simulation relations for enabling correct-by-design control refinement that are founded on coupling uncertainties of stochastic systems via sub-probability measures. The presented relations are essential for constructing abstract models that are related to not only one model but to a set of parameterized models. The results are demonstrated on three case studies, including a nonlinear and a high-dimensional system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_07428 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Bayesian Formal Synthesis of Unknown Systems via Robust Simulation Relations Schön, Oliver van Huijgevoort, Birgit Haesaert, Sofie Soudjani, Sadegh Systems and Control Logic in Computer Science This paper addresses the problem of data-driven computation of controllers that are correct by design for safety-critical systems and can provably satisfy (complex) functional requirements. With a focus on continuous-space stochastic systems with parametric uncertainty, we propose a two-stage approach that decomposes the problem into a learning stage and a robust formal controller synthesis stage. The first stage utilizes available Bayesian regression results to compute robust credible sets for the true parameters of the system. For the second stage, we introduce methods for systems subject to both stochastic and parametric uncertainties. We provide simulation relations for enabling correct-by-design control refinement that are founded on coupling uncertainties of stochastic systems via sub-probability measures. The presented relations are essential for constructing abstract models that are related to not only one model but to a set of parameterized models. The results are demonstrated on three case studies, including a nonlinear and a high-dimensional system. |
| title | Bayesian Formal Synthesis of Unknown Systems via Robust Simulation Relations |
| topic | Systems and Control Logic in Computer Science |
| url | https://arxiv.org/abs/2304.07428 |