Publication
Distributed model predictive control using optimality condition decomposition and community detection
Journal Article (2021)
Journal
Journal of Process Control
Pages
54-68
Volume
99
Doc link
https://doi.org/10.1016/j.jprocont.2021.01.007
File
Authors
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Segovia Castillo, Pau
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Puig Cayuela, Vicenç
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Duviella, Eric
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Etienne, Lucien
Abstract
This work regards the development of a distributed model predictive control strategy for large-scale systems, as centralized implementations often suffer from non-scalability. The decomposition of the overall system into minimally coupled subsystems as well as their coordination are based on optimality condition decomposition (OCD) and community detection. The OCD approach allows to solve the associated control subproblems in parallel in an iterative manner until the required degree of accuracy is attained. The proposed strategy is tested on two different systems, the quadruple-tank system and the Barcelona drinking water network, which allow to highlight the effectiveness of the approach.
Categories
optimisation, predictive control.
Author keywords
model predictive control, distributed control systems, water network
Scientific reference
P. Segovia, V. Puig, E. Duviella and L. Etienne. Distributed model predictive control using optimality condition decomposition and community detection. Journal of Process Control, 99: 54-68, 2021.
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