  Conventional control of fluid systems does not consider system-wide knowledge
for optimising energy efficient operation. Distributed control of fluid systems
combines reliable local control of components while using system-wide
cooperation to ensure energy efficient operation. The presented work compares
three approaches to distributed control based on multi-agent systems,
distributed model predictive control (DMPC), multi-agent deep reinforcement
learning (MADRL) and market mechanism design. These approaches were applied to
a generic fluid system and evaluated with regard to functionality, energy
efficient operation, modeling effort, reliability in the face of disruptions,
and transparency of control decisions. All approaches were shown to fulfil the
functionality, though a trade-off between functional quality and energy
efficiency was identified. Increased modeling effort was shown to improve the
performance slightly while a strong interdependence of information caused by
excessive information sharing has proven to be disadvantageous. DMPC and
partially observable MADRL were less sensitive to disruptions than market
mechanism. In conclusion, agent-based control of fluid systems achieves greater
energy efficiency than conventional methods, with values similar to centralized
optimal control and thus represent a viable design approach of fluid system
control.

Systems and Control;Systems and Control;Optimization and Control