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https://hdl.handle.net/2440/83133
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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 2168-2275 |
Statement of Responsibility: | 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 |
Published version: | http://dx.doi.org/10.1109/tcyb.2013.2263131 |
Appears in Collections: | Aurora harvest Electrical and Electronic Engineering publications |
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