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Type: Journal article
Title: Adaptive neural tracking control for a class of nonlinear systems with dynamic uncertainties
Author: Wang, H.
Shi, P.
Li, H.
Zhou, Q.
Citation: IEEE Transactions on Cybernetics, 2017; 47(10):3075-3087
Publisher: IEEE
Issue Date: 2017
ISSN: 2168-2267
Statement of
Huanqing Wang, Peng Shi, Hongyi Li, and Qi Zhou
Abstract: This paper considers the problem of adaptive neural control of nonlower triangular nonlinear systems with unmodeled dynamics and dynamic disturbances. The design difficulties appeared in the unmodeled dynamics and nonlower triangular form are handled with a dynamic signal and a variable partition technique for the nonlinear functions of all state variables, respectively. It is shown that the proposed controller is able to ensure the semi-global boundedness of all signals of the resulting closed-loop system. Furthermore, the system output is ensured to converge to a small domain of the given trajectories. The main advantage about this research is that a neural networks-based tracking control method is developed for uncertain nonlinear systems with unmodeled dynamics and nonlower triangular form. Simulation results demonstrate the feasibility of the newly presented design techniques.
Keywords: Adaptive neural networks control; backstepping; nonlower triangular nonlinear systems
Rights: © 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
DOI: 10.1109/TCYB.2016.2607166
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Appears in Collections:Aurora harvest 3
Electrical and Electronic Engineering publications

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