Computing Optimal Joint Chance Constrained Control Policies
Optimization and Control
2024-11-22 v2 Systems and Control
Systems and Control
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.
Cite
@article{arxiv.2312.10495,
title = {Computing Optimal Joint Chance Constrained Control Policies},
author = {Niklas Schmid and Marta Fochesato and Sarah H. Q. Li and Tobias Sutter and John Lygeros},
journal= {arXiv preprint arXiv:2312.10495},
year = {2024}
}