On convex problems in chance-constrained stochastic model predictive control

Eugenio Cinquemani; Mayank Agarwal; Debasish Chatterjee; John Lygeros

doi:10.1016/j.automatica.2011.01.023

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arXiv · 0905.3447

On convex problems in chance-constrained stochastic model predictive control

Eugenio Cinquemani·Mayank Agarwal·Debasish Chatterjee·John Lygeros

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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.

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Eugenio Cinquemani, Mayank Agarwal, Debasish Chatterjee, John Lygeros. 2009-05-21. On convex problems in chance-constrained stochastic model predictive control. https://doi.org/10.1016/j.automatica.2011.01.023

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