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Mathematical Problems in Engineering
Volume 2017, Article ID 7864375, 10 pages
Research Article

Neural Networks Approximator Based Robust Adaptive Controller Design of Hypersonic Flight Vehicles Systems Coupled with Stochastic Disturbance and Dynamic Uncertainties

School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China

Correspondence should be addressed to Xiuyu Zhang; moc.361@08uyuixgnahz

Received 9 March 2017; Accepted 8 August 2017; Published 18 September 2017

Academic Editor: Weihai Zhang

Copyright © 2017 Guoqiang Zhu 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.


A neural network robust control is proposed for a class of generic hypersonic flight vehicles with uncertain dynamics and stochastic disturbance. Compared with the present schemes of dealing with dynamic uncertainties and stochastic disturbance, the outstanding feature of the proposed scheme is that only one parameter needs to be estimated at each design step, so that the computational burden can be greatly reduced and the designed controller is much simpler. Moreover, by introducing a performance function in controller design, the prespecified transient and performance of tracking error can be guaranteed. It is proved that all signals of closed-loop system are uniformly ultimately bounded. The simulation results are carried out to illustrate effectiveness of the proposed control algorithm.