Aircraft Icing Detection, Identification, and Reconfigurable Control Based on Kalman Filtering and Neural Networks

Abstract

Flight in all weather conditions has necessitated correctly detecting icing and taking reasonable measures against it. This work aims to aid in detection and identification of airframe icing based on statistical properties of aircraft dynamics and reconfigurable control protecting aircraft from hazardous icing conditions. A Kalman filter is used for data collection in detecting icing, which aerodynamically deteriorates flight performance. A neural network process is applied for identifying the icing model of the aircraft, which is represented by 5 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 parameters affected by ice, which corresponds to the aircraft inverse dynamic model. The necessary training and validation set for the neural network model of the iced aircraft are obtained from simulations of the nominal model, which are performed for various icing conditions. In order to decrease noise effects on the states and increase training performance of the neural network, the estimated states by the Kalman filter are used. A suitable neural network model of aircraft inverse dynamics is obtained by using system identification methods and learning algorithms. This trained model is used as an application for control of an aircraft that has lost its controllability due to icing. The method is applied to F16 military and A340 commercial aircraft models and the results are adequate.