  This letter proposes a neural network (NN) aided remote unmanned aerial
vehicle (UAV) online control algorithm, coined oHJB. By downloading a UAV's
state, a base station (BS) trains an HJB NN that solves the
Hamilton-Jacobi-Bellman equation (HJB) in real time, yielding the optimal
control action. Initially, the BS uploads this control action to the UAV. If
the HJB NN is sufficiently trained and the UAV is far away, the BS uploads the
HJB NN model, enabling to locally carry out control decisions even when the
connection is lost. Simulations corroborate the effectiveness of oHJB in
reducing the UAV's travel time and energy by utilizing the trade-off between
uploading delays and control robustness in poor channel conditions.
