Title: Risk-averse model predictive control,
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.