TY - JOUR
T1 - Unmanned air vehicle air data estimation using a matrix of pressure sensors: a comparison of neural networks and look-up tables
AU - Samy, Ihab
AU - Postlethwaite, Ian
AU - Gu, Da-Wei
PY - 2011/6
Y1 - 2011/6
N2 - Flush airdata sensing (FADS) systems are cost- and weight- effective alternatives to current air data booms for measuring important air data parameters such as airspeed, angle of attack, sideslip, etc. Most applications consider large manned/unmanned air vehicles where the Pitot-static tube is located at the nose tip. However, traditional air data booms can be physically impractical for micro- (unmanned) air vehicles (MAVs) and, in this article, a FADS system mounted on the wing leading edge of a MAV flown at low speeds of Mach 0.07 (wind tunnel experiments under corresponding conditions) is designed. Moreover, two approaches for converting the FADS system pressure to meaningful air data are compared: a neural network (NN) approach and a look-up table (LUT). Results have shown that instrumentation weight and cost were reduced by 80 per cent and 97 per cent, respectively, in comparison to a traditional air data boom. Overall, the NN estimation accuracies were 0.51°, 0.44 lb/ft2, and 0.62 m/s and the LUT estimation accuracies 1.32°, 0.11 lb/ft2, and 0.88 m/s for the angle of attack, static pressure, and airspeed, respectively. It was also found that the LUT has faster execution times while the NN was in most cases more robust to sensor faults. However, while the LUT requires high memory usage, especially for higher dimensions, the NN can be executed in a few lines of code.
AB - Flush airdata sensing (FADS) systems are cost- and weight- effective alternatives to current air data booms for measuring important air data parameters such as airspeed, angle of attack, sideslip, etc. Most applications consider large manned/unmanned air vehicles where the Pitot-static tube is located at the nose tip. However, traditional air data booms can be physically impractical for micro- (unmanned) air vehicles (MAVs) and, in this article, a FADS system mounted on the wing leading edge of a MAV flown at low speeds of Mach 0.07 (wind tunnel experiments under corresponding conditions) is designed. Moreover, two approaches for converting the FADS system pressure to meaningful air data are compared: a neural network (NN) approach and a look-up table (LUT). Results have shown that instrumentation weight and cost were reduced by 80 per cent and 97 per cent, respectively, in comparison to a traditional air data boom. Overall, the NN estimation accuracies were 0.51°, 0.44 lb/ft2, and 0.62 m/s and the LUT estimation accuracies 1.32°, 0.11 lb/ft2, and 0.88 m/s for the angle of attack, static pressure, and airspeed, respectively. It was also found that the LUT has faster execution times while the NN was in most cases more robust to sensor faults. However, while the LUT requires high memory usage, especially for higher dimensions, the NN can be executed in a few lines of code.
KW - neural networks
KW - look-up table
KW - air data estimation
KW - matrix of pressure orifices
KW - unmanned air vehicles
U2 - 10.1177/0954410011399040
DO - 10.1177/0954410011399040
M3 - Article
SN - 0733-9429
SN - 0954-4100
SN - 1943-7900
SN - 2041-3025
VL - 225
SP - 807
EP - 820
JO - The Journal of Aerospace Engineering
JF - The Journal of Aerospace Engineering
IS - 7
ER -