Controlled Invariant Sets for Gaussian Process State Space Models
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917418534699008 |
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| author | Griffioen, Paul Zhong, Bingzhuo Arcak, Murat Zamani, Majid Caccamo, Marco |
| author_facet | Griffioen, Paul Zhong, Bingzhuo Arcak, Murat Zamani, Majid Caccamo, Marco |
| contents | We compute probabilistic controlled invariant sets for nonlinear systems using Gaussian process state space models, which are data-driven models that account for unmodeled and unknown nonlinear dynamics. We propose a semidefinite programming scheme for designing state-feedback controllers that maximize the probability of the trajectories staying within a probabilistic controlled invariant set while satisfying input constraints. The results are validated on a quadrotor, both in simulation and on a physical platform. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11256 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Controlled Invariant Sets for Gaussian Process State Space Models Griffioen, Paul Zhong, Bingzhuo Arcak, Murat Zamani, Majid Caccamo, Marco Systems and Control We compute probabilistic controlled invariant sets for nonlinear systems using Gaussian process state space models, which are data-driven models that account for unmodeled and unknown nonlinear dynamics. We propose a semidefinite programming scheme for designing state-feedback controllers that maximize the probability of the trajectories staying within a probabilistic controlled invariant set while satisfying input constraints. The results are validated on a quadrotor, both in simulation and on a physical platform. |
| title | Controlled Invariant Sets for Gaussian Process State Space Models |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2407.11256 |