Print Email Facebook Twitter Synthetic Air Data System for Pitot Tube Failure Detection on the Variable Skew Quad Plane Title Synthetic Air Data System for Pitot Tube Failure Detection on the Variable Skew Quad Plane Author Larocque, Frédéric (TU Delft Aerospace Engineering; TU Delft Control & Simulation) Contributor Smeur, E.J.J. (mentor) Remes, B.D.W. (graduation committee) De Ponti, T.M.L. (graduation committee) Degree granting institution Delft University of Technology Corporate name Delft University of Technology Programme Aerospace Engineering Date 2023-07-04 Abstract Pitot tube-free airspeed estimation methods exist for fixed-wing and multirotor configurations, but lack direct applicability to hybrid unmanned air vehicles due to their wide flight envelope and changing dynamics during transition. This work proposes a novel synthetic air data system for the Variable Skew Quad Plane (VSQP) hybrid vehicle to allow airspeed estimation from hover to high speed forward flight and provide pitot tube fault detection. An Extended Kalman Filter fuses Global Navigation Satellite System (GNSS) and inertial measurements using model-independent kinematics equations to estimate wind and airspeed without the use of the pitot tube. The filter is augmented by a simplified vehicle force model. Pitot tube fault detection is achieved with a simple thresholding operation on the pitot tube measurement and the airspeed estimation residual. Accurate airspeed estimation was validated with logged test flight data, achieving an overall 1.62 m/s root mean square error. Using the airspeed estimation, quick detection (0.16 s) of a real-life abrupt pitot tube fault was demonstrated. This new airspeed estimation method provides an innovative approach for increasing the fault tolerance of the VSQP and similar quad-plane vehicles. Subject variable skew quad planesynthetic air data systemairspeedHybrid vehiclesExtended Kalman Filterpitot tubeFault Detection To reference this document use: http://resolver.tudelft.nl/uuid:5d786e19-6871-4478-bda8-43f7cab20633 Part of collection Student theses Document type master thesis Rights © 2023 Frédéric Larocque Files PDF Thesis_frederic_larocque.pdf 7.75 MB Close viewer /islandora/object/uuid:5d786e19-6871-4478-bda8-43f7cab20633/datastream/OBJ/view