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In this paper, a hierarchical temporal multilayer
decentralised model predictive control (HTML-DMPC)
approach for drinking water networks (DWN) is proposed.
The upper temporal layer works with a daily scale and is in
charge of achieving the global objectives, which correspond
with the optimal selection of the sources and the path to
the reservoirs. On the other hand, the lower temporal layer
is in charge of manipulating the set-point of the actuators
to satisfy the local objectives, i.e., the minimisation of the
energy needed for pumping water to the reservoirs. The system
decomposition is based on graph partitioning theory, which
considers the graph representation of the DWN topology. The
obtained system decomposition allows to establish a hierarchical
flow of information between the MPC controllers. Hence, the
proposed DMPC strategy results in a hierarchical-like scheme.
Results obtained when used selected simulation scenarios show
the effectiveness of the proposed control strategy in terms of
system modularity, reduced computational burden and, at the
same time, the admissible loss of performance in contrast to a
centralised MPC (CMPC) strategy.
CitationPuig, V.; Ocampo-Martinez, C.; Montes de Oca, S. Hierarchical temporal multi-layer decentralised MPC strategy for drinking water networks: application to the Barcelona case study. A: Mediterranean Conference on Control and Automation. "20th Mediterranean Conference on Control & Automation". Barcelona: 2012, p. 734-739.
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