Dropout feedback parametrized policies for stochastic predictive controller

This article presents a novel control policy class for the networked control of linear dynamical systems with bounded inputs. The control channel is assumed to have i.i.d. Bernoulli packet dropouts. Our proposed class of policies is parametrized relative to the past dropouts. We show how to augment the underlying optimization problem with a constant negative drift constraint in order to ensure mean-square boundedness of the closed-loop states. The resulting convex quadratic program can be solved periodically online. The states of the closed loop plant under the receding horizon implementation of the proposed class of policies are mean square bounded for any positive bound on the control.