Background: Owing to rapid development of hardware components and reduction in prices,
Unmanned Ariel Vehicles (UAVs) are becoming ubiquitous including application to airborne ad-hoc
communication relay stations. Quadcopters are one class of UAVs, which have particularity seen rapid
growth due to their versatility. Since quadcopters are inherently unstable and hard to stabilize by human
operator, they need automated attitude stabilization. However, altitude stabilization around desired
height is often overlooked, while it has an important role in optimal location for airborne communication
Objective: The paper addresses the issue by developing controllers for UAV hovering. The proposed
approach can then be extended to arbitrary height (possibly with different sensor setup).
Method: For the development of control schemes, two approaches were used: PID and Neural network
(NN) based one. For the development of NN (i.e. learning phase) Gazebo simulation environment was
used essentially modeling the human driver. Developed approaches were tested both in simulation and
in real-world scenario on AR.Drone 1.0 and AR.Drone 2.0 UAVs.
Results: Obtained indoor results demonstrated PID accuracy of 1 cm with overshoot of 2.7% and settling
time of 3.75 s, while NN demonstrated 2.1 cm, 1%, and 8.4 s, respectively. Outdoor testing was
also performed with similar result trends.
Conclusion: Both developed controllers demonstrated good results (indoor and outdoor) and could be
used in real world scenario, but NN due to its favorable characteristics (i.e. human driver modelling)
and straightforward development phase (as compared to PID which involves lot of trial-and-error) is