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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 1401427, 16 pages
Research Article

Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

1School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
2Xi’an Aeronautics Computing Technique Research Institute, AVIC, Xi’an 710068, China

Correspondence should be addressed to Zhonghua Wu; moc.qq@575798364

Received 14 October 2016; Revised 28 March 2017; Accepted 11 April 2017; Published 17 May 2017

Academic Editor: Asier Ibeas

Copyright © 2017 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 study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller.