Application of Online Iterative Learning Tracking Control for Quadrotor UAVs

Abstract

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.