Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/72463
Title: Adaptive neural network control for strict-feedback nonlinear systems using backstepping design
Authors: Zhang, Tao
Ge, S.S. 
Hang, C.C. 
Issue Date: 1999
Source: Zhang, Tao,Ge, S.S.,Hang, C.C. (1999). Adaptive neural network control for strict-feedback nonlinear systems using backstepping design. Proceedings of the American Control Conference 2 : 1062-1066. ScholarBank@NUS Repository.
Abstract: This paper focuses on the adaptive control problem 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 backstepping design. The developed control scheme guarantees the uniform ultimate boundedness of the closed-loop adaptive systems. The relationship between the transient performance and the design parameters is given to guide the tuning of the controller.
Source Title: Proceedings of the American Control Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/72463
ISSN: 07431619
Appears in Collections:Staff Publications

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