Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/78498
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Type: Journal article
Title: Observer-based adaptive neural network control for nonlinear stochastic systems with time delay
Author: Zhou, Q.
Shi, P.
Xu, S.
Li, H.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2013; 24(1):71-80
Publisher: IEEE
Issue Date: 2013
ISSN: 2162-237X
2162-2388
Statement of
Responsibility: 
Qi Zhou, Peng Shi, Shengyuan Xu, and Hongyi Li
Abstract: This paper considers the problem of observer-based adaptive neural network (NN) control for a class of single-input single-output strict-feedback nonlinear stochastic systems with unknown time delays. Dynamic surface control is used to avoid the so-called explosion of complexity in the backstepping design process. Radial basis function NNs are directly utilized to approximate the unknown and desired control input signals instead of the unknown nonlinear functions. The proposed adaptive NN output feedback controller can guarantee all the signals in the closed-loop system to be mean square semi-globally uniformly ultimately bounded. Simulation results are provided to demonstrate the effectiveness of the proposed methods.
Keywords: Adaptive control; backstepping; dynamic surface control; fuzzy control; nonlinear systems
Rights: © 2012 IEEE
RMID: 0020124587
DOI: 10.1109/TNNLS.2012.2223824
Appears in Collections:Electrical and Electronic Engineering publications

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