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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 787931, 12 pages
Research Article

Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft

1School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
2Science and Technology on Aircraft Control Laboratory, AVIC Xi’an Flight Automatic Control Research Institute, Xi’an 710065, China

Received 28 July 2015; Accepted 17 September 2015

Academic Editor: Xinguang Zhang

Copyright © 2015 Zhonghua Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents an adaptive neural control for the longitudinal dynamics of a morphing aircraft. Based on the functional decomposition, it is reasonable to decompose the longitudinal dynamics into velocity and altitude subsystems. As for the velocity subsystem, the adaptive control is proposed via dynamic inversion method using neural network. To deal with input constraints, the additional compensation system is employed to help engine recover from input saturation rapidly. The highlight is that high order integral chained differentiator is used to estimate the newly defined variables and an adaptive neural controller is designed for the altitude subsystem where only one neural network is employed to approximate the lumped uncertain nonlinearity. The altitude subsystem controller is considerably simpler than the ones based on backstepping. It is proved using Lyapunov stability theory that the proposed control law can ensure that all the tracking error converges to an arbitrarily small neighborhood around zero. Numerical simulation study demonstrates the effectiveness of the proposed strategy, during the morphing process, in spite of some uncertain system nonlinearity.