  This paper introduces a novel data-driven hierarchical control scheme for
managing a fleet of nonlinear, capacity-constrained autonomous agents in an
iterative environment. We propose a control framework consisting of a
high-level dynamic task assignment and routing layer and low-level motion
planning and tracking layer. Each layer of the control hierarchy uses a
data-driven Model Predictive Control (MPC) policy, maintaining bounded
computational complexity at each calculation of a new task assignment or
actuation input. We utilize collected data to iteratively refine estimates of
agent capacity usage, and update MPC policy parameters accordingly. Our
approach leverages tools from iterative learning control to integrate learning
at both levels of the hierarchy, and coordinates learning between levels in
order to maintain closed-loop feasibility and performance improvement of the
connected architecture.
