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Type: Journal article
Title: Approximation-based adaptive neural control design for a class of nonlinear systems
Author: Chen, B.
Liu, K.
Liu, X.
Shi, P.
Lin, C.
Zhang, H.
Citation: IEEE Transactions on Cybernetics, 2014; 44(5):610-619
Publisher: IEEE
Issue Date: 2014
ISSN: 2168-2267
Statement of
Bing Chen, Kefu Liu, Xiaoping Liu, Peng Shi, Chong Lin, and Huaguang Zhang
Abstract: This paper focuses on approximation-based adaptive neural control of a class of nonlinear non-strict-feedback systems. Based on the structural characteristic and the monotonously increasing property of the system bounding functions, a variable separation method is first developed. By this method, an approximation-based adaptive backstepping approach is proposed for a class of nonlinear non-strict-feedback systems. It is shown that the proposed controller guarantees semi-global boundedness of all the signals in the closed-loop systems. Three examples are used to illustrate the effectiveness of the proposed approach.
Keywords: Adaptive neural control
function approximation technique
nonlinear systems
radial basis function neural networks.
Rights: © 2013 IEEE
DOI: 10.1109/TCYB.2013.2263131
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Appears in Collections:Aurora harvest
Electrical and Electronic Engineering publications

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