Please use this identifier to cite or link to this item:
|Title:||Adaptive neural network control for strict-feedback nonlinear systems using backstepping design||Authors:||Zhang, Tao
|Issue Date:||1999||Citation:||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|
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.