Trajectory Tracking of AR.Drone Quadrotor Using Fuzzy Logic Controller

Agung Prayitno, Veronica Indrawati, Gabriel Utomo


In this paper, Fuzzy Logic Controller (FLC) is implemented in the AR.Drone quadrotor in order to make it follow a given trajectory reference. The distance between the position and angle of the AR.Drone to the reference point is used as the input of FLC. As for the output, pitch value and yaw rate will be the controlling signal for the AR.Drone. The navigation data of the AR.Drone are forward speed (vx), sideward speed (vy), and yaw. These navigation data are going to be used to estimate positions and orientation of the AR.Drone. To compensate the y-position drift, the value of vyis also use as a criterion to determine the roll compensation. The FLC algorithm is implemented to AR.Drone 2.0 Elite Edition using LabVIEW software. Also, the algorithm has been tested in various trajectories such as straight line, a straight line with a perpendicular turn, a rectangular trajectory, and a curved trajectory. The results have shown that AR.Drone can follow a given trajectory with various initial position and orientation quite well.

Full Text:



Krajnik T, Vonasek V, Fiser D, Faigl J. AR-Drone as a Platform for Robotic Research and Education. Research and Eductation in Robotics :EUROBOT. Heidelberg, 2011; draft version.

Mary C, Totu L C, Koldbaek S K. Modeling and Control of Autonomous Quad-Rotor. Dept of Electronic Systems University of Aalborg Denmark. Project Report. 2010.

Jacco vand der Spek, Mario V. AR.Drone Autonomous Control and Position Determination. Bachelor Thesis. TU-Delft. 2012.

Pierre-Jean B, Francois C, David V, Nicolas P. The Navigation and Control Technology Inside the AR Drone Micro UAV. 18th IFAC World Congress, Milano Italy. 2011; reprint accessed on 11 August 2014

Stephane P, Nicolas B. AR.Drone Developer Guide. Parrot. SDK 1.6. 2011

Gerrard M. Modeling and Control of the Parrot AR.Drone. SEIT UNSW Canberra. Final Project Report. 2012.

Michael M. The AR Drone LabVIEW Toolkit: A Software Framework for the Control of Low Cost Quadrotor Aerial Robots. Master of Science Thesis. TUFTS University. 2012.

Sun Y. Modeling, Identification and Control of a Quadrotor Drone Using Low-Resolution Sensing. Master of Science Thesis. University of Illinois at Urbana-Champaign. 2012.

Guilherme V R, Manuel G O, Francisco R R. Nonlinear H Controller for the Quad-Rotor Helicopter with Input Coupling. 18th IFAC World Congress, Milano Italy. 2011; reprint.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120
Fax: +62 274 564604