  In this paper we present a framework for risk-averse model predictive control
(MPC) of linear systems affected by multiplicative uncertainty. Our key
innovation is to consider time-consistent, dynamic risk metrics as objective
functions to be minimized. This framework is axiomatically justified in terms
of time-consistency of risk preferences, is amenable to dynamic optimization,
and is unifying in the sense that it captures a full range of risk assessments
from risk-neutral to worst case. Within this framework, we propose and analyze
an online risk-averse MPC algorithm that is provably stabilizing. Furthermore,
by exploiting the dual representation of time-consistent, dynamic risk metrics,
we cast the computation of the MPC control law as a convex optimization problem
amenable to implementation on embedded systems. Simulation results are
presented and discussed.
