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Mathematical Problems in Engineering
Volume 2017, Article ID 6574527, 14 pages
https://doi.org/10.1155/2017/6574527
Research Article

T-S Fuzzy Modelling and Attitude Control for Hypersonic Gliding Vehicles

1Control Science and Control Engineering, Harbin Institute of Technology, Harbin 150000, China
2Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, 53851 Lappeenranta, Finland

Correspondence should be addressed to Weidong Zhang; moc.361@tnemomenifih

Received 28 December 2016; Accepted 21 March 2017; Published 23 May 2017

Academic Editor: Guangming Xie

Copyright © 2017 Weidong Zhang 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

This paper addresses the T-S fuzzy modelling and attitude control in three channels for hypersonic gliding vehicles (HGVs). First, the control-oriented affine nonlinear model has been established which is transformed from the reentry dynamics. Then, based on Taylor’s expansion approach and the fuzzy linearization approach, the homogeneous T-S local modelling technique for HGVs is proposed. Given the approximation accuracy and controller design complexity, appropriate fuzzy premise variables and operating points of interest are selected to construct the T-S homogeneous submodels. With so-called fuzzy blending, the original plant is transformed into the overall T-S fuzzy model with disturbance. By utilizing Lyapunov functional approach, a state feedback fuzzy controller has been designed based on relaxed linear matrix inequality (LMI) conditions to stable the original plants with a prescribed performance of disturbance. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed T-S fuzzy controller for the original attitude dynamics; the superiority of the designed T-S fuzzy controller compared with other local controllers based on the constructed fuzzy model is shown as well.