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. 1. Introduction Unmanned aerial vehicles (UAVs) have become very popular among researchers and developers in the last decade, owing to their capabilities of various applications such as meteorological surveillance, disaster monitoring, and military purposes. Depending on their applications, UAVs vary in sizes, shapes, and operating ranges. UAVs are complicated for control considering the nonlinearity of the system, no supervision of pilots, and external disturbances that need sophisticated control system to deal with. A quadrotor UAV is a special UAV that has four rotors with a symmetric shape to generate thrust. A quadrotor UAV can vertically take-off, hover, swiftly manoeuvre in any direction and carry a large payload comparing to its own weight. In addition, a quadrotor UAV is normally small compared with other types of UAVs, which makes it simple and cheap, and accessible indoors or in urban constrained areas. In order to achieve autonomous control, many studies and experiments have been performed for UAVs. However, due to their nonlinearity, multi-input and coupling characteristics, traditional control methods such as PID control may perform poorly under uncertainty and wind disturbances for UAVs, as shown in [1–3]. As an optimization technique with feedback control, Linear Quadratic Regulator (LQR) and its variation of State Dependent Riccati Equation (SDRE) control were proved to perform well for UAVs without disturbances [4, 5]. Comparisons and implementation of Sliding Mode Control (SMC) and Backstepping control were presented in [6–8]. The SMC methods were found to be robust against uncertainties, but they are still relatively complicated and
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