Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0005-1098(00)00116-3
Title: Adaptive neural network control for strict-feedback nonlinear systems using backstepping design
Authors: Zhang, T.
Ge, S.S. 
Hang, C.C. 
Issue Date: Dec-2000
Source: Zhang, T., Ge, S.S., Hang, C.C. (2000-12). Adaptive neural network control for strict-feedback nonlinear systems using backstepping design. Automatica 36 (12) : 1835-1846. ScholarBank@NUS Repository. https://doi.org/10.1016/S0005-1098(00)00116-3
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.
Source Title: Automatica
URI: http://scholarbank.nus.edu.sg/handle/10635/61756
ISSN: 00051098
DOI: 10.1016/S0005-1098(00)00116-3
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