Risk-averse model predictive control (MPC) offers a control framework that allows one to account for ambiguity in the knowledge of the underlying probability distribution and unifies stochastic and worst-case MPC. In this paper we study risk-averse MPC problems for constrained nonlinear Markovian switching systems using generic cost functions, and derive Lyapunov-type risk-averse stability conditions by leveraging the properties of risk-averse dynamic programming operators. We propose a controller design procedure to design risk-averse stabilizing terminal conditions for constrained nonlinear Markovian switching systems. Lastly, we cast the resulting risk-averse optimal control problem in a favorable form which can be solved efficiently and thus deems risk-averse MPC suitable for applications.
@article{arxiv.1704.00342,
title = {Risk-averse model predictive control},
author = {Pantelis Sopasakis and Domagoj Herceg and Alberto Bemporad and Panagiotis Patrinos},
journal= {arXiv preprint arXiv:1704.00342},
year = {2018}
}