  This work studies the standoff tracking problem to drive an unmanned aerial
vehicle (UAV) to slide on a desired circle over a moving target at a constant
height. We propose a novel Lyapunov guidance vector (LGV) field with tunable
convergence rates for the UAV's trajectory planning and a deep neural network
(DNN)-based model predictive control (MPC) scheme to track the reference
trajectory. Then, we show how to collect samples for training the DNN offline
and design an integral module (IM) to refine the tracking performance of our
DNN-based MPC. Moreover, the hardware-in-the-loop (HIL) simulation with an
FPGA@200MHz demonstrates that our method is a valid alternative to embedded
implementations of MPC for addressing complex systems and applications which is
impossible for directly solving the MPC optimization problems.
