Computing Optimal Joint Chance Constrained Control Policies

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, a...

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Veröffentlicht in:IEEE transactions on automatic control Jg. 70; H. 7; S. 4904 - 4911
Hauptverfasser: Schmid, Niklas, Fochesato, Marta, Li, Sarah H.Q., Sutter, Tobias, Lygeros, John
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Aerospace electronics
Constraints
Costs
Dynamic programming
Dynamic programming (DP)
Heuristic algorithms
joint chance constrained programming
Kernel
Optimal control
Optimization
Policies
Programming
Safety
stochastic optimal control
Stochastic processes
Trajectory
ISSN:0018-9286, 1558-2523
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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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2025.3546078