  Centralized control of a multi-agent system improves upon distributed control
especially when multiple agents share a common task e.g., sorting different
materials in a recycling facility. Traditionally, each agent in a sorting
facility is tuned individually which leads to suboptimal performance if one
agent is less efficient than the others. Centralized control overcomes this
bottleneck by leveraging global system state information, but it can be
computationally expensive. In this work, we propose a novel framework called
Longitudinal Control Volumes (LCV) to model the flow of material in a recycling
facility. We then employ a Kalman Filter that incorporates local measurements
of materials into a global estimation of the material flow in the system. We
utilize a model predictive control algorithm that optimizes the rate of
material flow using the global state estimate in real-time. We show that our
proposed framework outperforms distributed control methods by 40-100% in
simulation and physical experiments.
