Subsonic Tests of a Flush Air Data Sensing System Applied to a Fixed-Wing Micro Air Vehicle
作者： Ihab Samy , Ian Postlethwaite , Dawei Gu
DOI: 10.1007/S10846-008-9266-X
关键词:
摘要: Flush air data sensing (FADS) systems have been successfully tested on the nose tip of large manned/unmanned vehicles. In this paper we investigate application a FADS system wing leading edge micro (unmanned) vehicle (MAV) flown at speed as low Mach 0.07. The motivation behind project is driven by need to find alternative solutions booms which are physically impractical for MAVs. Overall an 80% and 97% decrease in instrumentation weight cost respectively were achieved. Air modelling implemented via radial basis function (RBF) neural network (NN) trained with extended minimum resource allocating (EMRAN) algorithm. Wind tunnel used train test NN, where estimation accuracies 0.51°, 0.44 lb/ft2 0.62 m/s achieved angle attack, static pressure wind respectively. Sensor faults investigated it was found that use autoassociative NN reproduce input improved robustness single multiple sensor faults. Additionally simple domain validity demonstrated how careful selection training set crucial accurate estimations.
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