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

TL;DR

This paper introduces a novel risk-averse model predictive control framework for linear systems with multiplicative uncertainty, emphasizing time-consistency and dynamic risk metrics, and provides algorithms with stability guarantees and practical convex optimization solutions.

Contribution

It develops a unified, axiomatic framework for time-consistent risk-averse MPC, including new algorithms and stability analysis for systems with uncertainty.

Findings

Proposed a stabilizing online risk-averse MPC algorithm.

Formulated control law computation as a convex optimization problem.

Validated the approach with simulation results.

Abstract

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.