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Distributed MPC for resource‐constrained control systems
Author(s) -
Scherer Helton,
Camponogara Eduardo,
NormeyRico Júlio,
Álvarez José Domingo,
Guzmán José Luis
Publication year - 2014
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2151
Subject(s) - controller (irrigation) , model predictive control , constraint (computer aided design) , computer science , set (abstract data type) , control theory (sociology) , coupling (piping) , distributed generation , distributed element model , control (management) , resource (disambiguation) , energy (signal processing) , control engineering , distributed computing , engineering , mathematics , renewable energy , artificial intelligence , agronomy , biology , programming language , computer network , mechanical engineering , statistics , electrical engineering
SUMMARY This article presents a distributed model predictive control methodology to manage energy resources for a set of consumer subsystems. The objective of the controller is to optimally distribute the allowable energy to the subsystems. The proposed methodology yields a distributed solution that converges to the optimum that would be obtained by a centralized controller. This optimal performance is achieved by expressing the problem in terms of slack variables and the global coupling constraint as a set of local subsystem constraints, thereby favoring the application of distributed model predictive control. Hardware‐in‐the‐loop experiments with an air‐conditioning thermal solar plant are performed to show the good performance of the proposed distributed controller. Copyright © 2014 John Wiley & Sons, Ltd.