Decentralized Model Predictive Control of Drinking Water Networks using an Automatic Subsystem Decomposition Approach

This paper proposes an automatic model decomposition approach for decentralized model predictive control (DMPC) of drinking water networks (DWNs). For a given DWN, the proposed algorithm partitions the network in a set of subnetworks by taking advantage of the topology of the network, of the information about the use of actuators, and of system management heuristics. The derived suboptimal DMPC strategy results in a hierarchical solution with a set of MPC controllers used for each partition. A comparative study between centralized MPC (CMPC) and DMPC approaches is described for the considered case study, which consists of an aggregate version of the Barcelona DWN. Results on several simulation scenarios show the effectiveness of the proposed DMPC approach in terms of the reduced computation burden and, at the same time, of the admissible lost of performance.