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.

Iterative Learning Control (ILC) is a technique of tracking control aiming at improving tracking performance for systems that work in a repetitive mode. ILC is a simple and effective control and can progressively reduce tracking errors and improve system performance from iteration to iteration. In this paper, we first classify the ILC schemes into three categories: offline learning scheme, online learning scheme, and online-offline learning scheme. In each scheme, P-type, D-type, PD-type, and switching gain learning control are discussed. The corresponding convergence conditions for each type of ILCs are presented. Then, different ILCs are applied to control a general nonlinear system with noise and disturbance. After that, various ILC schemes are tested under different test conditions to compare the effectiveness and robustness. It is demonstrated that the online-offline type ILCs can obtain the best tracking performance, and the switching gain learning control can provide the fastest convergence speed.

Quadrotor unmanned aerial vehicles (UAVs) are recognized to be capable of various tasks including search and rescue and surveillance for their agilities and small sizes. This paper proposes a simple and robust quadrotor controller utilizing online Iterative Learning Control (ILC) that is known to be useful for tasks performed repeatedly. The controller is used for trajectory tracking to perform a variety of manoeuvring such as take-off, landing, smooth translation, and circular trajectory motion. Different online ILCs are studied and simulation results prove the ability to gain full autonomy and perform successfully certain missions in the presence of considerably large disturbances.