Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/83164
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Type: Journal article
Title: Adaptive neural control of pure-feedback nonlinear time-delay systems via dynamic surface technique
Author: Wang, M.
Liu, X.
Shi, P.
Citation: IEEE Transactions on Cybernetics, 2011; 41(6):1681-1692
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Issue Date: 2011
ISSN: 1083-4419
1941-0492
Statement of
Responsibility: 
Min Wang, Xiaoping Liu, and Peng Shi
Abstract: This paper is concerned with robust stabilization problem for a class of nonaffine pure-feedback systems with unknown time-delay functions and perturbed uncertainties. Novel continuous packaged functions are introduced in advance to remove unknown nonlinear terms deduced from perturbed uncertainties and unknown time-delay functions, which avoids the functions with control law to be approximated by radial basis function (RBF) neural networks. This technique combining implicit function and mean value theorems overcomes the difficulty in controlling the nonaffine pure-feedback systems. Dynamic surface control (DSC) is used to avoid “the explosion of complexity” in the backstepping design. Design difficulties from unknown time-delay functions are overcome using the function separation technique, the Lyapunov-Krasovskii functionals, and the desirable property of hyperbolic tangent functions. RBF neural networks are employed to approximate desired virtual controls and desired practical control. Under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced significantly, and semiglobal uniform ultimate boundedness of all of the signals in the closed-loop system is guaranteed. Simulation studies are given to demonstrate the effectiveness of the proposed design scheme.
Rights: © 2011 IEEE
DOI: 10.1109/TSMCB.2011.2159111
Appears in Collections:Aurora harvest
Electrical and Electronic Engineering publications

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