In this paper we present a framework for riskaverse 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 riskneutral 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.
In REF the authors provide stability proofs for a linear MPC controller, which minimizes time-consistent risk metrics in a convex optimization form.
A framework for time-consistent, risk-averse model predictive control: Theory and algorithms
2014 American Control Conference
2014 American Control Conference
Computer Science Mathematics