Iterative Learning Control and its Applications

Robotic manipulators and Unmanned Aerial Vehicles (UAVs) have been used to execute some repeatable assignments, due to the advantage of safety, convenience, and flexibility. Iterative learning control (ILC) is an approach to eliminate some repeatable disturbance which may come from unknown parameters, dynamic uncertainties, or the surroundings. Therefore, this research aims to present two types of iterative learning controller, PD-type and adaptive-type, to implement on robotic manipulator and UAVs, which would complete the given repetitive missions and achieve the expected specifications. Meanwhile, a dead zone inverse model is proposed to solve the actuator dead zone problem. The traditional hierarchical control method for UAVs is adopted. The inner loop control performance is verified using Gimbal. The free flight experimental test is completed with the purpose of certifying the proposed out loop control stratagem. In addition, theoretical proof and simulation results are also presented to demonstrate the effectiveness of the proposed controllers.