Neural Network Based Model Reference Adaptive Attitude Control for a Micro Unmanned Air Vehicle

During recent decades, Unmanned Aerial Vehicles (UAVs) has increased their success in several areas of applications thanks to their versatility. Furthermore, the growing availability of low-cost electronics has pushed their use in civil consumer applications. Attitude control is one of the needed tasks to effectively carry out missions, whose accuracy and adaptability to high payload imbalances or atmospheric disturbances are fundamental requirements. This paper focuses on the design of a flight control scheme based on Model Reference Adaptive Control with Neural Networks. The effectiveness of the proposed controller is assessed through experimental tests carried out on a Crazyflie 2.1 quadrotor.