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

Standoff Tracking Using DNN-Based MPC with Implementation on FPGA