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Miniature rotorcraft-based Unmanned Aerial Vehicles (UAVs) have received a growing interest in both industrial and academic research. Thanks to their hover and vertical take-off and landing (VTOL) capabilities, they are indeed particularly well suited for many civil missions such as video supervision of road traffic, surveillance of urban districts, victims localization after natural disasters, fire detection or building inspection for maintenance. Design of guidance navigation and control algorithms for the autonomous flight of small rotorcraft-based UAVs is a challenging research area because of their nonlinear dynamics and their high sensitivity to aerodynamic perturbations. Various control strategies such as backstepping (Bouabdallah & Siegwart, 2005), (Frazzoli et al., 2000), (Mahony & Hamel, 2004), nonlinear model predictive control (Kim et al., 2002), (Bertrand et al., 2007a) or sliding modes (Bouabdallah & Siegwart, 2005) have been successfully applied to stabilization or trajectory tracking of UAV models. Nevertheless most of them require full state knowledge for feedback control design. For robotic systems it may be useful, for cost or payload reasons, to limit the number of embedded sensors. For a miniature UAV, the nature of the mission itself may also directly impact the choice of the sensors that will be used, and therefore the type of measurements that will be available for the vehicle control. In constrained environments, for example, the use of a vision based sensor may be preferred to a GPS to estimate the relative position of the vehicle with respect to its environment. In that case, linear velocity measurements may not be available. Another example is the case of a test bench design, where a “ready-to-use” radio controlled vehicle is used along with external sensors that do not require structural modifications of the vehicle. Such external sensors are for example motion capture systems (Kondak et al., 2007), (Kundak & Mettler, 2007), (Valenti et al., 2006), or magnetic field based sensors (Castillo et al., 2004). With such equipments, only the position and the attitude angles of the vehicle can be directly measured. Nevertheless, knowledge of the vehicle state components (positions, linear velocities, attitude angles and angular velocities) is required for control. A practical approach may consist in computing the velocities from the position measurements by finite differentiations. This method is used in (Kondak et al., 2007) to

Stabilization of Scale Model Vertical Take-off and Landing Vehicles without Velocity Measurements