Fixed wing Unmanned Aerial Vehicles (UAVs) are an increasingly common sensing platform, owing to their key advantages: speed, endurance and ability to explore remote areas. While these platforms are highly efficient, they cannot easily be equipped with air data sensors commonly found on their larger scale manned counterparts. Indeed, such sensors are bulky, expensive and severely reduce the payload capability of the UAVs. In consequence, UAV controllers (humans or autopilots) have little information on the actual mode of operation of the wing (normal, stalled, spin) which can cause catastrophic losses of control when flying in turbulent weather conditions. In this article, we propose a real-time air parameter estimation scheme that can run on commercial, low power autopilots in real-time. The computational method is based on a hybrid decomposition of the modes of operation of the UAV. A Bayesian approach is considered for estimation, in which the estimated airspeed, angle of attack and sideslip are described statistically. An implementation on a UAV is presented, and the performance and computational efficiency of this method are validated using hardware in the loop (HIL) simulation and experimental flight data and compared with classical Extended Kalman Filter estimation. Our benchmark tests shows that this method is faster than EKF by up to two orders of magnitude.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A hybrid system approach to airspeed, angle of attack and sideslip estimation in Unmanned Aerial Vehicles


    Contributors:


    Publication date :

    2015-06-01


    Size :

    1451430 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Neural Network Based Approach to Helicopter Low Airspeed and Sideslip Angle Estimation

    McCool, K. / Haas, D. / Schaefer, C. et al. | British Library Conference Proceedings | 1996


    A neural network based approach to helicopter low airspeed and sideslip angle determination

    McCool, Kelly / Haas, David / Schaefer, Jr., Carl | AIAA | 1996



    Prediction of Helicopter Airspeed and Sideslip Angle in the Low Speed Environment

    McCool, K. / Haas, D. / American Helicopter Society | British Library Conference Proceedings | 1997


    PROBELESS AIRSPEED AND ANGLE OF ATTACK MEASUREMENTS FOR PROPELLER DRIVEN VEHICLES

    CAMPBELL KIP GREGORY | European Patent Office | 2021

    Free access