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.
Original language | English |
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Pages (from-to) | 281 |
Number of pages | 8 |
Journal | Automatica |
Volume | 100 |
Early online date | 04 Dec 2018 |
DOIs | |
Publication status | Published - Feb 2019 |
Keywords
- Risk measures
- Nonlinear Markovian switching systems
- Model predictive control