Game approach to distributed model predictive control

L. Giovanini

Research output: Contribution to journalArticle

23 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
LanguageEnglish
Pages1729-1739
Number of pages11
JournalIET Control Theory and Applications
Volume5
Issue number15
DOIs
Publication statusPublished - Oct 2011

Fingerprint

Dynamic Games
Model predictive control
Model Predictive Control
Control Algorithm
Potential Games
Game
Optimization Problem
Control Sets
Coupled Problems
Decentralized Control
Heat Exchanger
Control Problem
Decentralized control
Heat exchangers
Framework

Keywords

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

Cite this

Giovanini, L. / Game approach to distributed model predictive control. In: IET Control Theory and Applications . 2011 ; Vol. 5, No. 15. pp. 1729-1739 .
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Game approach to distributed model predictive control. / Giovanini, L.

In: IET Control Theory and Applications , Vol. 5, No. 15, 10.2011, p. 1729-1739 .

Research output: Contribution to journalArticle

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