18. Data-Driven Evolutionary-Game-Based Control for Drinking-Water Networks

This work addresses the design of a control strategy for drinking-water transport networks (DWTNs) based on evolutionary-game theory (EGT). This theory allows to model the evolution of a population composed by a large and finite number of rational agents, which are able to make decisions. As an analogy with a multi-variable control system for DWTN, the whole population represents the total available water resource in the system, and each agent represents a small portion of the resource. In the population evolution, each agent makes the decision to select one of the system valves and/or pumps in order to establish its corresponding value of resource. Agents make these decisions pursuing an improvement of their benefits described by a fitness function, which is associated to the control objective, i.e., agents receive more benefits as the control objective is achieved. This global objective in the DWTN is established by the company in charge of the management of the network, e.g., maintain safety volumes within the tanks, minimize the water costs, minimize the costs of the energy to operate the actuators. The aforementioned evolution process, in which agents make decisions, is used to solve an optimization problem that is described in terms of current measurements of the DWTN tank volumes and subject to constraints over the decision variables in the system (physical limits of flows through valves and pumps). Furthermore, since the control problem is given in terms of instant measurements, this control strategy might be implemented without the need of an explicit model of the DWTN. In this work, two different data-driven population-games-based control designs for DWTNs are presented, and both the necessary assumptions and conditions to implement the proposed methodologies are clearly stated. Finally, the effectiveness of the proposed control approach through the system performance improvement is shown by using the considered DWTN case study.