Data-Driven Permissible Safe Control with Barrier Certificates
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911866125549568 |
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| author | Mazouz, Rayan Skovbekk, John Mathiesen, Frederik Baymler Frew, Eric Laurenti, Luca Lahijanian, Morteza |
| author_facet | Mazouz, Rayan Skovbekk, John Mathiesen, Frederik Baymler Frew, Eric Laurenti, Luca Lahijanian, Morteza |
| contents | This paper introduces a method of identifying a maximal set of safe strategies from data for stochastic systems with unknown dynamics using barrier certificates. The first step is learning the dynamics of the system via Gaussian process (GP) regression and obtaining probabilistic errors for this estimate. Then, we develop an algorithm for constructing piecewise stochastic barrier functions to find a maximal permissible strategy set using the learned GP model, which is based on sequentially pruning the worst controls until a maximal set is identified. The permissible strategies are guaranteed to maintain probabilistic safety for the true system. This is especially important for learning-enabled systems, because a rich strategy space enables additional data collection and complex behaviors while remaining safe. Case studies on linear and nonlinear systems demonstrate that increasing the size of the dataset for learning the system grows the permissible strategy set. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00136 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven Permissible Safe Control with Barrier Certificates Mazouz, Rayan Skovbekk, John Mathiesen, Frederik Baymler Frew, Eric Laurenti, Luca Lahijanian, Morteza Machine Learning Robotics Systems and Control This paper introduces a method of identifying a maximal set of safe strategies from data for stochastic systems with unknown dynamics using barrier certificates. The first step is learning the dynamics of the system via Gaussian process (GP) regression and obtaining probabilistic errors for this estimate. Then, we develop an algorithm for constructing piecewise stochastic barrier functions to find a maximal permissible strategy set using the learned GP model, which is based on sequentially pruning the worst controls until a maximal set is identified. The permissible strategies are guaranteed to maintain probabilistic safety for the true system. This is especially important for learning-enabled systems, because a rich strategy space enables additional data collection and complex behaviors while remaining safe. Case studies on linear and nonlinear systems demonstrate that increasing the size of the dataset for learning the system grows the permissible strategy set. |
| title | Data-Driven Permissible Safe Control with Barrier Certificates |
| topic | Machine Learning Robotics Systems and Control |
| url | https://arxiv.org/abs/2405.00136 |