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Computing Optimal Joint Chance Constrained Control Policies

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2025

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Schmid, Niklas
Fochesato, Marta
Li, Sarah H.Q.
Lygeros, John

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https://doi.org/10.1109/tac.2025.3546078
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European Union (EU): 787845
Swiss National Science Foundation: 51NF40_225155

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IEEE Transactions on Automatic Control. IEEE. 2025, 70(7), S. 4904-4911. ISSN 0018-9286. eISSN 1558-2523. Verfügbar unter: doi: 10.1109/tac.2025.3546078

Zusammenfassung

We consider the problem of optimally controlling stochastic, Markovian systems subject to joint chance constraints over a finite-time horizon. For such problems, standard dynamic programming is inapplicable due to the time correlation of the joint chance constraints, which calls for non-Markovian, and possibly stochastic, policies. Hence, despite the popularity of this problem, solution approaches capable of providing provably optimal and easy-to-compute policies are still missing. We fill this gap by augmenting the dynamics via a binary state, allowing us to characterize the optimal policies and develop a dynamic programming-based solution method.

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Dynamic programming (DP), joint chance constrained programming, stochastic optimal control

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ISO 690SCHMID, Niklas, Marta FOCHESATO, Sarah H.Q. LI, Tobias SUTTER, John LYGEROS, 2025. Computing Optimal Joint Chance Constrained Control Policies. In: IEEE Transactions on Automatic Control. IEEE. 2025, 70(7), S. 4904-4911. ISSN 0018-9286. eISSN 1558-2523. Verfügbar unter: doi: 10.1109/tac.2025.3546078
BibTex
@article{Schmid2025-07Compu-73956,
  title={Computing Optimal Joint Chance Constrained Control Policies},
  year={2025},
  doi={10.1109/tac.2025.3546078},
  number={7},
  volume={70},
  issn={0018-9286},
  journal={IEEE Transactions on Automatic Control},
  pages={4904--4911},
  author={Schmid, Niklas and Fochesato, Marta and Li, Sarah H.Q. and Sutter, Tobias and Lygeros, John}
}
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