Evolutionary-games approach for distributed predictive control involving resource allocation

Julian Barreiro-Gomez, Germán Obando, Carlos Ocampo-Martinez, Nicanor Quijano

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This study proposes a distributed model predictive control (DMPC) scheme based on population games for a system formed by a set of sub-systems. In addition to considering independent operational constraints for each sub-system, the controller addresses a coupled constraint that involves the sum of all control inputs. This constraint models an upper bound on the total amount of energy supplied to the plant. The proposed approach does not need a centralised coordinator when having a coupled constraint involving all the decision variables. The proposed methodology, which takes advantage of evolutionary game theory concepts, provides an optimal solution for the described problem. Moreover, it is shown that the methodology has plug- and-play features, i.e. for each already designed local MPC controller nothing changes when more sub-systems are added/ removed to/from the global constrained control problem. Furthermore, the stability analysis of the proposed DMPC scheme is presented.

Original languageEnglish (US)
Pages (from-to)772-782
Number of pages11
JournalIET Control Theory and Applications
Volume13
Issue number6
DOIs
StatePublished - Apr 16 2019

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Control and Optimization
  • Electrical and Electronic Engineering

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