Inflight Parameter Identification and Icing Location Detection of the Aircraft: The Time-Varying Case

Abstract

This paper considers inflight parameter identification and icing location detection of the aircraft in a more common time-varying nature. In particular, ice accumulation is modeled as a continuous process, and the effect of the ice upon aircraft dynamics is to be accreted with time. Time-varying case of the Hinf algorithm is implemented to provide inflight estimate of aircraft dynamic parameters, and the estimated results are delivered to a probabilistic neural network to decide icing location of the aircraft; an excitation measure of the aircraft is also adopted in the network input layer. A database corresponding to different icing cases and severities was generated for the training and test of the detection network. Based on the test results, the icing detection framework presented in this paper is believed to be with promising applicableness for our further studies.