Risk-averse model predictive control
arXiv:1704.00342 · doi:10.1016/j.automatica.2018.11.022
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.
Cited by in corpus (6)
- Online Learning Based Risk-Averse Stochastic MPC of Constrained Linear Uncertain Systems
- Risk-Averse Model Predictive Operation Control of Islanded Microgrids
- Data-driven distributionally robust MPC for constrained stochastic systems
- An Integrated Transportation Distance Between Kernels and Approximate Dynamic Risk Evaluation in Markov Systems
- Infinite-horizon Risk-constrained Linear Quadratic Regulator with Average Cost
- Safe Sampling-Based Air-Ground Rendezvous Algorithm for Complex Urban Environments