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Globally decentralized adaptive backstepping neural network tracking control for unknown nonlinear interconnected systems
Author(s) -
Chen Weisheng,
Li Junmin
Publication year - 2010
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.160
Subject(s) - backstepping , control theory (sociology) , bounded function , a priori and a posteriori , artificial neural network , nonlinear system , decentralised system , residual , computer science , adaptive control , tracking error , domain (mathematical analysis) , set (abstract data type) , uniform boundedness , control (management) , mathematics , artificial intelligence , algorithm , mathematical analysis , philosophy , physics , epistemology , quantum mechanics , programming language
Abstract A globally stable decentralized adaptive backstepping neural network tracking control scheme is designed for a class of large‐scale systems with mismatched interconnections. Under the assumption that the subsystems share the reference signals from the other subsystems, neural networks are used to approximate the unknown interconnections dependent on all reference signals such that the NN approximation domain can be determined a priori based on the bounds of reference signals. The proposed control approach can guarantee that all closed‐loop signals are globally uniformly ultimately bounded and that the tracking errors converge to a small residual set around the origin. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society