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Complexity
Volume 2017, Article ID 6034786, 14 pages
https://doi.org/10.1155/2017/6034786
Research Article

A Novel SHLNN Based Robust Control and Tracking Method for Hypersonic Vehicle under Parameter Uncertainty

1School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang 471023, China
2School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT9 5AH, UK
3Beijing Aerospace Automation Control Institute, Beijing 100854, China
4Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
5School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Correspondence should be addressed to Zhile Yang; ku.ca.buq@70gnayz

Received 6 July 2017; Accepted 11 September 2017; Published 17 October 2017

Academic Editor: Guang Li

Copyright © 2017 Chuanfeng Li 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.

Abstract

Hypersonic vehicle is a typical parameter uncertain system with significant characteristics of strong coupling, nonlinearity, and external disturbance. In this paper, a combined system modeling approach is proposed to approximate the actual vehicle system. The state feedback control strategy is adopted based on the robust guaranteed cost control (RGCC) theory, where the Lyapunov function is applied to get control law for nonlinear system and the problem is transformed into a feasible solution by linear matrix inequalities (LMI) method. In addition, a nonfragile guaranteed cost controller solved by LMI optimization approach is employed to the linear error system, where a single hidden layer neural network (SHLNN) is employed as an additive gain compensator to reduce excessive performance caused by perturbations and uncertainties. Simulation results show the stability and well tracking performance for the proposed strategy in controlling the vehicle system.