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Decentralized output-feedback neural control for systems with unknown interconnections.

Authors
  • Chen, Weisheng
  • Li, Junmin
Type
Published Article
Journal
IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
Publication Date
Feb 01, 2008
Volume
38
Issue
1
Pages
258–266
Identifiers
DOI: 10.1109/TSMCB.2007.904544
PMID: 18270097
Source
Medline
License
Unknown

Abstract

An adaptive backstepping neural-network control approach is extended to a class of large-scale nonlinear output-feedback systems with completely unknown and mismatched interconnections. The novel contribution is to remove the common assumptions on interconnections such as matching condition, bounded by upper bounding functions. Differentiation of the interconnected signals in backstepping design is avoided by replacing the interconnected signals in neural inputs with the reference signals. Furthermore, two kinds of unknown modeling errors are handled by the adaptive technique. All the closed-loop signals are guaranteed to be semiglobally uniformly ultimately bounded, and the tracking errors are proved to converge to a small residual set around the origin. The simulation results illustrate the effectiveness of the control approach proposed in this correspondence.

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