Abstract
In this paper, the “wing rock” phenomenon is described for slender delta 80° wing aircrafts on the roll axis. This phenomenon causes the aircraft to undergo a strong oscillatory movement with amplitude dependent on the angle of attack. The objective is to identify “wing rock” using the Patchy Neural Network (PNN), which is a new form of neural nets. For the update of the weights of the network, an observer called RISE (Robust Integral of Sign Error) and equations of algebraic form are used. This causes the PNN to be fast, efficient and of a low computational cost.
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Chavatzopoulos, P.M., Giotis, T., Christodoulou, M., Psillakis, H. (2012). Using RISE Observer to Implement Patchy Neural Network for the Identification of “Wing Rock” Phenomenon on Slender Delta 80° Wings. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_27
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DOI: https://doi.org/10.1007/978-3-642-32909-8_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32908-1
Online ISBN: 978-3-642-32909-8
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