| Sumario: | In this chapter, a multi-layer decentralized model predictive control (ML-DMPC) approach is proposed and designed for its application to large-scale networked systems (LSNS). This approach is based on the periodic nature of the system disturbance and the availability of both static and dynamic models of the LSNS. Hence, the topology of the controller is structured in two layers. First, an upper layer is in charge of achieving the global objectives from a set O of control objectives given for the LSNS. This layer works with a sampling time Δt1, corresponding to the disturbances period. Second, a lower layer, with a sampling time Δt2, Δt1 > Δt2, is in charge of computing the references for the system actuators in order to satisfy the local objectives from the set of control objectives O. A system partitioning allows to establish a hierarchical flow of information between a set C of controllers designed based on model predictive control (MPC). Therefore, the whole proposed ML-DMPC strategy results in a centralized optimization problem for considering the global control objectives, followed of a decentralized scheme for reaching the local control objectives. The proposed approach is applied to a real case study: the water transport network of Barcelona (Spain). Results obtained with selected simulation scenarios show the effectiveness of the proposed ML-DMPC strategy in terms of system modularity, reduced computational burden and, at the same time, the admissible loss of performance with respect to a centralized MPC (CMPC) strategy. © Springer Science+Business Media Dordrecht 2014.
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