This book aims at the detection and identification of airframe
icing based on statistical properties of aircraft dynamics and
reconfigurable control protecting aircraft from hazardous icing
conditions. Icing model of aircraft is represented by five
parameters for iced wing airfoils. Icing is detected by a Kalman
filtering innovation approach. A neural network structure is
embodied such that its inputs are the aircraft estimated
measurements, and its outputs are the icing parameters. The
necessary training and validation set for the neural network model
of the iced aircraft are obtained from the simulations, which are
performed for various icing conditions. In order to decrease noise
effects on the states and to increase training performance of the
neural network, the estimated states by the Kalman filter are used.
A suitable neural network model of the iced aircraft is obtained by
using system identification methods and learning algorithms. This
trained network model is used as an application for the control of
the aircraft that has lost its controllability due to icing. The
method is applied to F16 military and A340 commercial aircraft
models.
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