Game approach to distributed model predictive control

L. Giovanini

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

This study introduces a framework for distributed model predictive control (MPC) based on dynamic games, where centralised and decentralised control algorithms can be viewed as dynamical games with coupled control sets. The original optimisation problem is decomposed into smaller coupled optimisation problems in a distributed structure, which is solved iteratively. Then, the resulting dynamic game is analysed using the theory of potential games to derive the properties of the resulting algorithms. This sheds new light on the properties of existing MPC algorithms and allows us to establish a unified
framework to analyse them. The control problem of a heat-exchanger network (HEN) is used to illustrate the effectiveness, practicality and limitations of the proposed framework
Original languageEnglish
Pages (from-to)1729-1739
Number of pages11
JournalIET Control Theory and Applications
Volume5
Issue number15
DOIs
Publication statusPublished - Oct 2011

Keywords

  • receding horizon control
  • networks
  • coordination
  • MPC
  • systems
  • optimization
  • game approach
  • distributed model
  • predictive control

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