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arXiv · 1703.01029

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

Sumeet Singh·Yin-Lam Chow·Anirudha Majumdar·Marco Pavone
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Abstract

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

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Sumeet Singh, Yin-Lam Chow, Anirudha Majumdar, Marco Pavone. 2017-03-03. A Framework for Time-Consistent, Risk-Sensitive Model Predictive Control: Theory and Algorithms. https://arxiv.org/abs/1703.01029

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