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In this paper we present a framework for risk-averse model predictive control(MPC) of linear systems affected by multiplicative uncertainty. Our keyinnovation is to consider time-consistent, dynamic risk metrics as objectivefunctions to be minimized. This framework is axiomatically justified in termsof time-consistency of risk preferences, is amenable to dynamic optimization,and is unifying in the sense that it captures a full range of risk assessmentsfrom risk-neutral to worst case. Within this framework, we propose and analyzean 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 problemamenable to implementation on embedded systems. Simulation results arepresented and discussed.

A Framework for Time-Consistent, Risk-Averse Model Predictive Control: Theory and Algorithms