Dionysos.jl: a Modular Platform for Smart Symbolic Control
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917921080475648 |
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| author | Calbert, Julien Banse, Adrien Legat, Benoît Jungers, Raphaël M. |
| author_facet | Calbert, Julien Banse, Adrien Legat, Benoît Jungers, Raphaël M. |
| contents | We introduce Dionysos.jl, a modular package for solving optimal control problems for complex dynamical systems using state-of-the-art and experimental techniques from symbolic control, optimization, and learning. More often than not with Cyber-Physical systems, the only sensible way of developing a controller is by discretizing the different variables, thus transforming the control task into a purely combinatorial problem on a finite-state mathematical object, called an abstraction of this system. Although this approach offers a safety-critical framework, the available techniques suffer important scalability issues. In order to render these techniques practical, it is necessary to construct smarter abstractions that differ from classical techniques by partitioning the state-space in a non trivial way. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14114 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dionysos.jl: a Modular Platform for Smart Symbolic Control Calbert, Julien Banse, Adrien Legat, Benoît Jungers, Raphaël M. Systems and Control We introduce Dionysos.jl, a modular package for solving optimal control problems for complex dynamical systems using state-of-the-art and experimental techniques from symbolic control, optimization, and learning. More often than not with Cyber-Physical systems, the only sensible way of developing a controller is by discretizing the different variables, thus transforming the control task into a purely combinatorial problem on a finite-state mathematical object, called an abstraction of this system. Although this approach offers a safety-critical framework, the available techniques suffer important scalability issues. In order to render these techniques practical, it is necessary to construct smarter abstractions that differ from classical techniques by partitioning the state-space in a non trivial way. |
| title | Dionysos.jl: a Modular Platform for Smart Symbolic Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2404.14114 |