# Online Deep Learning for Improved Trajectory Tracking of Unmanned Aerial Vehicles Using Expert Knowledge

## TL;DR

This paper introduces an online deep learning control method for UAV trajectory tracking that incorporates expert knowledge, enabling real-time adaptation without requiring an exact system model, resulting in improved performance.

## Contribution

It presents a novel online learning framework combining deep neural networks and expert rules for UAV control, enhancing robustness and adaptability over traditional offline methods.

## Key findings

- Online learning improves trajectory tracking accuracy.
- The method adapts to system variations and uncertainties.
- Experimental results outperform offline-trained controllers.

## Abstract

This work presents an online learning-based control method for improved trajectory tracking of unmanned aerial vehicles using both deep learning and expert knowledge. The proposed method does not require the exact model of the system to be controlled, and it is robust against variations in system dynamics as well as operational uncertainties. The learning is divided into two phases: offline (pre-)training and online (post-)training. In the former, a conventional controller performs a set of trajectories and, based on the input-output dataset, the deep neural network (DNN)-based controller is trained. In the latter, the trained DNN, which mimics the conventional controller, controls the system. Unlike the existing papers in the literature, the network is still being trained for different sets of trajectories which are not used in the training phase of DNN. Thanks to the rule-base, which contains the expert knowledge, the proposed framework learns the system dynamics and operational uncertainties in real-time. The experimental results show that the proposed online learning-based approach gives better trajectory tracking performance when compared to the only offline trained network.