On convex problems in chance-constrained stochastic model predictive control

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.

References in corpus (1)

• Stochastic control up to a hitting time: optimality and rolling-horizon implementation

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