Stochastic model predictive control for constrained discrete-time Markovian switching systems

Panagiotis Patrinos, Pantelis Sopasakis, Haralambos Sarimveis, Alberto Bemporad

Research output: Contribution to journalArticle

50 Citations (Scopus)
16 Downloads (Pure)

Abstract

In this paper we study constrained stochastic optimal control problems for Markovian switching systems, an extension of Markovian jump linear systems (MJLS), where the subsystems are allowed to be nonlinear. We develop appropriate notions of invariance and stability for such systems and provide terminal conditions for stochastic model predictive control (SMPC) that guarantee mean-square stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the SMPC law under very weak assumptions. In the special but important case of constrained MJLS we present an algorithm for computing explicitly the SMPC control law off-line, that combines dynamic programming with parametric piecewise quadratic optimization.
Original languageEnglish
Pages (from-to)2504-2514
Number of pages11
JournalAutomatica
Volume50
Issue number10
Early online date11 Sep 2014
DOIs
Publication statusPublished - 01 Oct 2014
Externally publishedYes

Keywords

  • Model predictive control
  • Stochastic Model Predictive Control
  • Markov Chains
  • Markovian switching systems
  • Stochastic control
  • Mean square stability
  • Uniform invariance
  • LMI
  • Linear matrix inequalities

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