The aim of this thesis is to develop a Learning Model Predictive Control (LMPC) framework for quadrotors; this work results to be one of the first comprehensive studies on LMPC applied to quadrotors in real-case scenarios. LMPC is a novel control technique that autonomously learns to improve its performances by collecting data coming from past executions of the control task. Given a certain task, the control algorithm makes the quadrotor to perform it repetitively; information coming from these repetitive iterations is progressively collected and employed by the controller to obtain better performances for the required task. This control method is especially versatile and useful for time-sensitive operations, in which the drone has to make fast and dexterous maneuvers in constrained and cluttered environments.