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Adaptive neural network control for strict-feedback nonlinear systems using backstepping design

Authors
  • Zhang, T.
  • Ge, S.S.
  • Hang, C.C.
Type
Published Article
Journal
Automatica
Publisher
Elsevier
Publication Date
Jan 01, 2000
Volume
36
Issue
12
Pages
1835–1846
Identifiers
DOI: 10.1016/S0005-1098(00)00116-3
Source
Elsevier
Keywords
License
Unknown

Abstract

This paper focuses on adaptive control of strict-feedback nonlinear systems using multilayer neural networks (MNNs). By introducing a modified Lyapunov function, a smooth and singularity-free adaptive controller is firstly designed for a first-order plant. Then, an extension is made to high-order nonlinear systems using neural network approximation and adaptive backstepping techniques. The developed control scheme guarantees the uniform ultimate boundedness of the closed-loop adaptive systems. In addition, the relationship between the transient performance and the design parameters is explicitly given to guide the tuning of the controller. One important feature of the proposed NN controller is the highly structural property which makes it particularly suitable for parallel processing in actual implementation. Simulation studies are included to illustrate the effectiveness of the proposed approach.

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