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Neural Computing and Applications

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01.12.2014 | Original Article

Adaptive neural output feedback control for stochastic nonlinear time-delay systems with unknown control directions

verfasst von: Zhaoxu Yu, Shugang Li, Hongbin Du

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2014

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Abstract

This paper first focuses on the problem of adaptive output feedback stabilization for a more general class of stochastic nonlinear time-delay systems with unknown control directions. By using a linear state transformation, the original system is transformed to a new system for which control design becomes feasible. Then a novel adaptive neural network (NN) output feedback control strategy, which only contains one adaptive parameter, is developed for such systems by combining the input-driven filter design, the backstepping technique, the NN’s parameterization, the Nussbaum gain function method and the Lyapunov–Krasovskii approach. The proposed control design guarantees that all signals in the closed-loop systems are 4-moment (or 2-moment) semi-globally uniformly bounded. Finally, two simulation examples are given to demonstrate the effectiveness and the applicability of the proposed control design.

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Titel: Adaptive neural output feedback control for stochastic nonlinear time-delay systems with unknown control directions
verfasst von: Zhaoxu Yu
Shugang Li
Hongbin Du
Publikationsdatum: 01.12.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1686-x

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