The use of autonomous vehicles, for a wide variety of applications, has been increasing during the latest years. Land-based vehicles can be used for many purposes, but are not as versatile as could be desired because they are dependent on the terrain. Aerial vehicles, such as aeroplanes and helicopters, do not depend on the terrain in the area of operation, as the land based vehicle. An autonomous helicopter has an advantage in maneuverability compared to an autonomous aeroplane, which is not able to hover (stand still in the air). This and the ability to take off and land in limited spaces are clear advantages of the autonomous helicopter. An autonomous helicopter is a versatile platform for a wide variety of applications. It can be used in situations as agricultural crop dusting, search and rescue missions, iection of bridges or power lines, surveillance of larger areas etc. Autonomous helicopter research and development has also been increasing the latest years. Yamaha Motors have, since the presentation of the autonomous model helicopter RCASS prototype in 1986, been developing autonomous model helicopters which are used commercially today. These helicopters are mainly used for crop dusting. Lots of unmanned aerial vehicles (UAV) research groups now try to find the ways of controlling model helicopters and test them. Berkeley UAV research group, Aalborg university UAV group, Georgia Tech Aerial Robotics Team … are some well known groups. They have tested different controllers on their UAVs and the operation of some of the controllers has been quite acceptable. This project aims to provide a methodology to design an efficient controller for a rotorcraft UAV. For the controller design, the dynamic model of the helicopter is first derived. A helicopter exhibits very complicated multi-input multi-output, nonlinear, time-varying and coupled dynamics, exposed to severe exogenous disturbances. This poses considerable difficulties for the identification, control and general operation. Using the dynamic equations of motion, we can find and derive a socalled Minimum-Complexity Helicopter Simulation Math Model and then simulate this model by Simulink. We should then verify the model and this can be done through some standard tests. To handle the problem of controlling such a complicated model, a hierarchical strategy is proposed, which consists of two controllers. Keywords UAV, Hierarchical Control, Nonlinear Model Predictive Control, LQ Regulator, Extended Kalman Filter