  This paper introduces deep neural networks (DNNs) as add-on blocks to
baseline feedback control systems to enhance tracking performance of arbitrary
desired trajectories. The DNNs are trained to adapt the reference signals to
the feedback control loop. The goal is to achieve a unity map between the
desired and the actual outputs. In previous work, the efficacy of this approach
was demonstrated on quadrotors; on 30 unseen test trajectories, the proposed
DNN approach achieved an average impromptu tracking error reduction of 43% as
compared to the baseline feedback controller. Motivated by these results, this
work aims to provide platform-independent design guidelines for the proposed
DNN-enhanced control architecture. In particular, we provide specific
guidelines for the DNN feature selection, derive conditions for when the
proposed approach is effective, and show in which cases the training efficiency
can be further increased.
