Open AccessNeural network‐based multivariable fixed‐time terminal sliding mode control for re‐entry vehicles
Author(s) -
Wang Xiao,
Guo Jie,
Tang Shengjing
Publication year - 2018
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2017.1309
Subject(s) - control theory (sociology) , multivariable calculus , robustness (evolution) , terminal sliding mode , artificial neural network , computer science , bounded function , singularity , sliding mode control , mathematics , control engineering , engineering , control (management) , nonlinear system , artificial intelligence , physics , quantum mechanics , mathematical analysis , biochemistry , chemistry , gene
This study develops a neural network (NN)‐based multivariable fixed‐time terminal sliding mode control (MFTTSMC) strategy for re‐entry vehicles (RVs) with uncertainties. A coupled MFTTSMC scheme is designed for the attitude system on the basis of feedback linearisation. A saturation function is introduced to avoid the singularity problem. Adaptive NNs are employed to approximate the uncertainties in RVs, thus alleviating chattering without sacrificing robustness. The whole closed‐loop system is proven to be bounded and tracking errors are fixed‐time stable. Simulations verify the effectiveness of the proposed strategy.