Risk-averse model predictive control

Pantelis Sopasakis, Domagoj Herceg, Alberto Bemporad, Panagiotis Patrinos

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
94 Downloads (Pure)

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 languageEnglish
Pages (from-to)281
Number of pages8
JournalAutomatica
Volume100
Early online date04 Dec 2018
DOIs
Publication statusPublished - Feb 2019

Keywords

  • Risk measures
  • Nonlinear Markovian switching systems
  • Model predictive control

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