Aircraft icing detection, identification and reconfigurable control based on kalman filtering and neural networks

In military and commercial aviation, the flight in all weather conditions has necessitated the correctly detecting icing and taking reasonable measures against it This work aims at the detection and identification of airframe icing based on statistical properties of aircraft dynamics and reconfigurable control protecting aircraft from hazardous king conditions. Icing model of aircraft is represented by five parameters based on past experiments for iced wing airfoils. Icing is detected by a Kalman filtering innovation sequence 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 mathematical models and the results are promising.