# Deep Neural Networks for Improved, Impromptu Trajectory Tracking of Quadrotors

arXiv: https://arxiv.org/abs/1610.06283 · HTML: https://mlsanity.com/paper/1610.06283/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2016.

## Built on by (most cited first, 9 of 9)

- [Guidance and Control Networks with Periodic Activation Functions](https://mlsanity.com/paper/2405.18084/) (2024)
- [DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models](https://mlsanity.com/paper/2409.11292/) (2024)
- [Simulation-Aided Policy Tuning for Black-Box Robot Learning](https://mlsanity.com/paper/2411.14246/) (2024)
- [System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach](https://mlsanity.com/paper/2505.10678/) (2025)
- [Uncertainty quantification in neural network classifiers -- a local linear approach](https://mlsanity.com/paper/2303.07114/) (2023)
- [Improving the Region of Attraction of a Multi-rotor UAV by Estimating Unknown Disturbances](https://mlsanity.com/paper/2409.00257/) (2024)
- [A highly maneuverable flying squirrel drone with agility-improving foldable wings](https://mlsanity.com/paper/2504.09609/) (2025)
- [Deep Q-Learning-Based Gain Scheduling for Nonlinear Quadcopter Dynamics](https://mlsanity.com/paper/2603.03127/) (2026)
- [Online Reinforcement Learning for Safe Gain Scheduling in Nonlinear Quadrotor Control](https://mlsanity.com/paper/2604.16819/) (2026)

## Abstract (arXiv)

Trajectory tracking control for quadrotors is important for applications ranging from surveying and inspection, to film making. However, designing and tuning classical controllers, such as proportional-integral-derivative (PID) controllers, to achieve high tracking precision can be time-consuming and difficult, due to hidden dynamics and other non-idealities. The Deep Neural Network (DNN), with its superior capability of approximating abstract, nonlinear functions, proposes a novel approach for enhancing trajectory tracking control. This paper presents a DNN-based algorithm as an add-on module that improves the tracking performance of a classical feedback controller. Given a desired trajectory, the DNNs provide a tailored reference input to the controller based on their gained experience. The input aims to achieve a unity map between the desired and the output trajectory. The motivation for this work is an interactive "fly-as-you-draw" application, in which a user draws a trajectory on a mobile device, and a quadrotor instantly flies that trajectory with the DNN-enhanced control system. Experimental results demonstrate that the proposed approach improves the tracking precision for user-drawn trajectories after the DNNs are trained on selected periodic trajectories, suggesting the method's potential in real-world applications. Tracking errors are reduced by around 40-50% for both training and testing trajectories from users, highlighting the DNNs' capability of generalizing knowledge.