Joint Chance Constrained Optimal Control via Linear Programming
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929347689971712 |
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| author | Schmid, Niklas Fochesato, Marta Sutter, Tobias Lygeros, John |
| author_facet | Schmid, Niklas Fochesato, Marta Sutter, Tobias Lygeros, John |
| contents | We establish a linear programming formulation for the solution of joint chance constrained optimal control problems over finite time horizons. The joint chance constraint may represent an invariance, reachability or reach-avoid specification that the trajectory must satisfy with a predefined probability. For finite state and action spaces, the solution is exact and our method computationally superior to approaches in the literature. For continuous state or action spaces, our linear programming formulation enables basis function approximations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_19360 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Joint Chance Constrained Optimal Control via Linear Programming Schmid, Niklas Fochesato, Marta Sutter, Tobias Lygeros, John Optimization and Control Systems and Control We establish a linear programming formulation for the solution of joint chance constrained optimal control problems over finite time horizons. The joint chance constraint may represent an invariance, reachability or reach-avoid specification that the trajectory must satisfy with a predefined probability. For finite state and action spaces, the solution is exact and our method computationally superior to approaches in the literature. For continuous state or action spaces, our linear programming formulation enables basis function approximations. |
| title | Joint Chance Constrained Optimal Control via Linear Programming |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2402.19360 |