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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 591789, 10 pages
Research Article

Neural Network-Based Adaptive Backstepping Control for Hypersonic Flight Vehicles with Prescribed Tracking Performance

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China

Received 1 December 2014; Revised 11 April 2015; Accepted 15 April 2015

Academic Editor: Hak-Keung Lam

Copyright © 2015 Zhu Guoqiang and Liu Jinkun. 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.


An adaptive neural control scheme is proposed for a class of generic hypersonic flight vehicles. The main advantages of the proposed scheme include the following: (1) a new constraint variable is defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries; (2) RBF NNs are employed to compensate for complex and uncertain terms to solve the problem of controller complexity; (3) only one parameter needs to be updated online at each design step, which significantly reduces the computational burden. It is proved that all signals of the closed-loop system are uniformly ultimately bounded. Simulation results are presented to illustrate the effectiveness of the proposed scheme.