Novel Multiagent Model-Predictive Control Performance Indices for Monitoring of a Large-Scale Distributed Water System

High-order, often distributed, dynamical systems composed of several interconnected subsystems are often referred to as large-scale systems (LSSs). LSSs are often hard to control with a single centralized controller due to the complexity imposed by the system's dimensionality and distributedness. As a result, decentralized or hierarchical control schemes are employed in controlling LSSs. Control performance assessment (CPA) is an important strategy to analyze the efficiency of controllers in LSSs. This paper presents CPA for the Rhine–Meuse Delta water system in The Netherlands. The water system consists of a large number of rivers and sea outlets with barriers and sluices. A flood in this area can damage the ecosystem and cities around it. Thus, it is essential to control this LSS in a way to protect the distributed water system against floods. For this purpose, a multiagent predictive control is developed to control the subsystems in the LSS. Further, two novel control performance indices (CPIs) based on the model-predictive control strategy are introduced to monitor the performance of the controllers and detect any changes in the system. Finally, the root cause of controller deficiencies is diagnosed. The suggested CPIs are compared with a historical performance index. Simulation results show the ability and effectiveness of the proposed CPIs in comparison with the performance measure used in the past.