On convex problems in chance-constrained stochastic model predictive control
Optimization and Control
2011-07-07 v1
Abstract
We investigate constrained optimal control problems for linear stochastic dynamical systems evolving in discrete time. We consider minimization of an expected value cost over a finite horizon. Hard constraints are introduced first, and then reformulated in terms of probabilistic constraints. It is shown that, for a suitable parametrization of the control policy, a wide class of the resulting optimization problems are convex, or admit reasonable convex approximations.
Cite
@article{arxiv.0905.3447,
title = {On convex problems in chance-constrained stochastic model predictive control},
author = {Eugenio Cinquemani and Mayank Agarwal and Debasish Chatterjee and John Lygeros},
journal= {arXiv preprint arXiv:0905.3447},
year = {2011}
}