# Risk-averse model predictive control

## TL;DR

This paper develops a risk-averse model predictive control framework for constrained nonlinear Markovian switching systems, providing stability conditions and an efficient controller design method that accounts for distributional ambiguity.

## Contribution

It introduces a novel stability analysis and controller design procedure for risk-averse MPC applied to nonlinear Markovian switching systems, unifying stochastic and worst-case approaches.

## Key findings

- Derived Lyapunov-type stability conditions for risk-averse MPC.
- Proposed a controller design procedure for risk-averse stabilization.
- Formulated the control problem for efficient computational solution.

## Abstract

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.