Convex computation of regions of attraction from data using Sums-of-Squares programming
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912804974362624 |
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| author | Khattabi, Oumayma Tacchi, Matteo Olaru, Sorin |
| author_facet | Khattabi, Oumayma Tacchi, Matteo Olaru, Sorin |
| contents | This paper focuses on the analysis of the Region of Attraction (RoA) for unknown autonomous dynamical systems. A data-driven approach based on the moment-Sum-of-Squares (SoS) hierarchy is proposed, enabling novel RoA outer approximations despite the reduced information on the dynamics. The main contribution consists of bypassing the system model and, hence, the recurring constraint on its polynomial structure. Numerical experiments showcase the influence of data on learned approximating sets, highlighting the potential of this method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14073 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Convex computation of regions of attraction from data using Sums-of-Squares programming Khattabi, Oumayma Tacchi, Matteo Olaru, Sorin Systems and Control Optimization and Control This paper focuses on the analysis of the Region of Attraction (RoA) for unknown autonomous dynamical systems. A data-driven approach based on the moment-Sum-of-Squares (SoS) hierarchy is proposed, enabling novel RoA outer approximations despite the reduced information on the dynamics. The main contribution consists of bypassing the system model and, hence, the recurring constraint on its polynomial structure. Numerical experiments showcase the influence of data on learned approximating sets, highlighting the potential of this method. |
| title | Convex computation of regions of attraction from data using Sums-of-Squares programming |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2507.14073 |