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

arXiv: https://arxiv.org/abs/1511.06981 · HTML: https://mlsanity.com/paper/1511.06981/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2015.

## Built on by (most cited first, 4 of 4)

- [Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers](https://mlsanity.com/paper/2205.00562/) (2022)
- [Risk-Averse Reinforcement Learning via Dynamic Time-Consistent Risk Measures](https://mlsanity.com/paper/2301.05981/) (2023)
- [Recursive Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model](https://mlsanity.com/paper/2506.00286/) (2025)
- [A Risk-Averse Preview-based Q-Learning Algorithm: Application to Highway Driving of Autonomous Vehicles](https://mlsanity.com/paper/2112.03232/) (2021)

## Abstract (arXiv)

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.