Open AccessNeural network-based backstepping design for the synchronization of cross-strict feedback hyperchaotic systems with unmatched uncertainties
Author(s) -
Haiyan Li,
Yi Hu,
Jin Ren,
Min Zhu,
Liang Liu
Publication year - 2012
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.61.140502
Subject(s) - backstepping , control theory (sociology) , synchronization (alternating current) , computer science , bounded function , scheme (mathematics) , artificial neural network , adaptive control , control (management) , mathematics , artificial intelligence , computer network , channel (broadcasting) , mathematical analysis
For a class of cross-strict feedback hyperchaotic systems with unmatched uncertainties, a multilayer neural network (MNN) based adaptive backstepping design method is proposed. An MNN is introduced to estimate the uncertainties in systems. Sliding mode and adaptive backstepping control are used to deal with the unmatched uncertainties and the MNN approximation errors. If the virtual control coefficients do not pass through zeros, the proposed method guarantees that the synchronization errors of the systems approach zeros. If the virtual control coefficients pass through zeros, the proposed method guarantees that the synchronization errors of the systems are bounded. Numerical simulations are given to demonstrate the efficiency of the proposed control scheme.