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Neural Computing and Applications

Erschienen in:

01.11.2015 | Original Article

Adaptive neural control with intercepted adaptation for time-delay saturated nonlinear systems

verfasst von: Shigen Gao, Bin Ning, Hairong Dong

Erschienen in: Neural Computing and Applications | Ausgabe 8/2015

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Abstract

In this paper, the adaptive neural control is proposed for a class of single-input-single-output nonlinear systems with state delay and input saturation. An intercepted adaptation approach is designed to attenuate the effect caused by the input saturation based on a constructed auxiliary system, and radial basis function neural networks are used in the online learning of unknown dynamics. Lyapunov–Krasovskii function is introduced to deal with the state delay. The proposed control scheme can guarantee semi-globally uniformly boundedness of the closed-loop system as rigorously proved by Lyapunov stability theorem. The ultimate and transient tracking errors will be confined in compact regions. The diameters of these regions can be adjusted to be arbitrarily small by tuning proper design parameters. Illustrative examples are used to demonstrate the effectiveness of the proposed control method.

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Titel: Adaptive neural control with intercepted adaptation for time-delay saturated nonlinear systems
verfasst von: Shigen Gao
Bin Ning
Hairong Dong
Publikationsdatum: 01.11.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 8/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-1855-6

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