Cooperation-Based Explicit Distributed Model Predictive Control

2013 ◽  
Vol 380-384 ◽  
pp. 707-711 ◽  
Author(s):  
Guo Qi Zhong ◽  
Zhi Yuan Liu
Keyword(s):  
Linear System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Distillation Column ◽  
Control Method ◽  
Parametric Programming ◽  
Distributed Model ◽  
Control Information ◽  
Distributed Model Predictive Control ◽  
Global Cost

in this paper, an explicit distributed model predictive control method for a class of linear system with control information coupling by means of multi-parametric programming is established. In order to get close to the optimal performance of centralized MPC, the method is based on cooperation, which solving weighted global cost instead of local ones. The method is employed on distillation column control problem to verify the efficiency.

scholarly journals A Coalitional Distributed Model Predictive Control Perspective for a Cyber-Physical Multi-Agent Application

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4041
Author(s):  
Anca Maxim ◽  
Constantin-Florin Caruntu
Keyword(s):  
Model Predictive Control ◽  
Communication Networks ◽  
Predictive Control ◽  
Technological Development ◽  
Distributed Model ◽  
Multi Agent Systems ◽  
Test Scenario ◽  
Distributed Model Predictive Control ◽  
Multi Agent ◽  
And Performance

Following the current technological development and informational advancement, more and more physical systems have become interconnected and linked via communication networks. The objective of this work is the development of a Coalitional Distributed Model Predictive Control (C- DMPC) strategy suitable for controlling cyber-physical, multi-agent systems. The motivation behind this endeavour is to design a novel algorithm with a flexible control architecture by combining the advantages of classical DMPC with Coalitional MPC. The simulation results were achieved using a test scenario composed of four dynamically coupled sub-systems, connected through an unidirectional communication topology. The obtained results illustrate that, when the feasibility of the local optimization problem is lost, forming a coalition between neighbouring agents solves this shortcoming and maintains the functionality of the entire system. These findings successfully prove the efficiency and performance of the proposed coalitional DMPC method.

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