# Design of Deep Neural Networks as Add-on Blocks for Improving Impromptu Trajectory Tracking

arXiv: https://arxiv.org/abs/1705.10932 · HTML: https://mlsanity.com/paper/1705.10932/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2017.

## Built on by (most cited first, 1 of 1)

- [Federated Learning for Data-Driven Feedforward Control: A Case Study on Vehicle Lateral Dynamics](https://mlsanity.com/paper/2503.02693/) (2025)

## Abstract (arXiv)

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.