Abstract
This paper presents a novel architecture for air-data angle estimation. It represents an effective low-cost low-weight solution to be implemented in small, mini and micro Unmanned Aerial Vehicles (UAVs). It can be used as a simplex sensor or as a voter in a dual-redundant sensor systems, to detect inconsistencies of the main sensors and accommodate the failures. The estimator acts as a virtual sensor processing data derived from an Attitude Heading Reference System (AHRS) coupled with a dynamic pressure sensor. This novel architecture is based on the synergy of a neural network and of an ANFIS filter which acts on the noise-corrupted data,cancelling the noise contribution without interfering with the turbulence frequencies, which must be preserved as key information for the AFCS activity.
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Battipede, M., Cassaro, M., Gili, P., Lerro, A. (2013). Novel Neural Architecture for Air Data Angle Estimation. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_32
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DOI: https://doi.org/10.1007/978-3-642-41013-0_32
Publisher Name: Springer, Berlin, Heidelberg
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