IRI - An evolutionary-games approach for distributed predictive control involving resource allocation

Publication

An evolutionary-games approach for distributed predictive control involving resource allocation

Journal Article (2019)

Journal

IET Control Theory and Applications

Pages

772-782

Volume

Number

Doc link

http://dx.doi.org/10.1049/iet-cta.2018.5716

File

Download the digital copy of the doc pdf document

Authors

Barreiro-Gomez, Julian
Obando, Germán
Ocampo Martínez, Carlos A.
Quijano Silva, Nicanor

Abstract

This paper 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 centralized 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.

Author keywords

Evolutionary game theory, predictive control, distributed control schemes

Scientific reference

J. Barreiro-Gomez, G. Obando, C. Ocampo-Martínez and N. Quijano. An evolutionary-games approach for distributed predictive control involving resource allocation. IET Control Theory and Applications, 13(6): 772-782, 2019.