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ISA Trans. 2009 Oct;48(4):468-75. doi: 10.1016/j.isatra.2009.05.004. Epub 2009 Jun 26.

Adaptive NN output-feedback stabilization for a class of stochastic nonlinear strict-feedback systems.

Author information

1
Department of Applied Mathematics, Xidian University, Xi'an, PR China. xidianjing@126.com

Abstract

In this paper, the adaptive neural network output-feedback stabilization problem is investigated for a class of stochastic nonlinear strict-feedback systems. The nonlinear terms, which only depend on the system output, are assumed to be completely unknown, and only an NN is employed to compensate for all unknown upper bounding functions, so that the designed controller is more simple than the existing results. It is shown that, based on the backstepping method and the technique of nonlinear observer design, the closed-loop system can be proved to be asymptotically stable in probability. The simulation results demonstrate the effectiveness of the proposed control scheme.

PMID:
19560144
DOI:
10.1016/j.isatra.2009.05.004
[Indexed for MEDLINE]

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