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arXiv · 2312.10495

Computing Optimal Joint Chance Constrained Control Policies

Niklas Schmid·Marta Fochesato·Sarah H. Q. Li·Tobias Sutter·John Lygeros
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Abstract

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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Niklas Schmid, Marta Fochesato, Sarah H. Q. Li, Tobias Sutter, John Lygeros. 2023-12-16. Computing Optimal Joint Chance Constrained Control Policies. https://arxiv.org/abs/2312.10495

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