Table of Contents
ISRN Robotics
Volume 2013, Article ID 476153, 20 pages
Research Article

Application of Online Iterative Learning Tracking Control for Quadrotor UAVs

Department of Aerospace Engineering, Ryerson University, Toronto, ON, Canada

Received 21 April 2013; Accepted 20 May 2013

Academic Editors: Z. Bi, J.-S. Liu, R. Safaric, and Y. Zhou

Copyright © 2013 Pong-in Pipatpaibul and P. R. Ouyang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Quadrotor unmanned aerial vehicles (UAVs) have attracted considerable interest for various applications including search and rescue, environmental monitoring, and surveillance because of their agilities and small sizes. This paper proposes trajectory tracking control of UAVs utilizing online iterative learning control (ILC) methods that are known to be powerful for tasks performed repeatedly. PD online ILC and switching gain PD online ILC are used to perform a variety of manoeuvring such as take-off, smooth translation, and various circular trajectory motions in two and three dimensions. Simulation results prove the ability and effectiveness of the online ILCs to perform successfully certain missions in the presence of disturbances and uncertainties. It also demonstrates that the switching gain PD ILC is much effective than the PD online ILC in terms of fast convergence rates and smaller tracking errors.