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

Yin-Lam Chow; Marco Pavone

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arXiv · 1511.06981

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

Yin-Lam Chow·Marco Pavone

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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.

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BibTeX RIS

Yin-Lam Chow, Marco Pavone. 2015-11-22. A Framework for Time-Consistent, Risk-Averse Model Predictive Control: Theory and Algorithms. https://arxiv.org/abs/1511.06981

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