Unmanned Aircraft Systems (UAS) have gained impetus in the last decade from increasing civil, scientific, military, commercial and recreational applications (e.g. urban traffic monitoring, war zone defense and monitoring, archaeological site prospection, inspection of electrical power lines, etc.). Autonomous flight, management and control of Unmanned Aerial Vehicles (UAVs) are important issues in many professional and research applications. This paper focuses on trajectory measurement and control using two sensors (Angle of Attack, AOA and Angle of Sideslip, AOS) for speed and attitude estimation. The sensors involved in the analysis are a pressure (static and dynamic) sensor and an Inertial Measurement Unit (IMU). Kalman Filtering (KF) has been applied for measurement noise removal and data fusion. The theoretical analysis of the KF shows that global exponential stability of the estimation error is achieved under these conditions. The method has been tested using experimental data from a small quadrotor, with a legacy autopilot to provide basic estimates of UAV velocity and attitude and comparing them with the sensors data. AOA and AOS have been validated via correlation between the AOA estimate and vertical accelerometer measurements, since lift force can be modeled as a linear function of AOA in normal flight. Results from several flight tests confirm the validity of the approach to trajectory determination.
File in questo prodotto:
Non ci sono file associati a questo prodotto.