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SCIENCE CHINA Information Sciences, Volume 60, Issue 12: 120202(2017) https://doi.org/10.1007/s11432-017-9113-8

Robust neural output-feedback stabilization for stochastic nonlinear processwith time-varying delay and unknown dead zone

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  • ReceivedApr 12, 2017
  • AcceptedMay 28, 2017
  • PublishedNov 9, 2017

Abstract

This article investigates the output-feedback control ofa class of stochastic nonlinear system with time-varying delay and unknown dead zone. Arobust neural stabilizing algorithm is proposed by using the circle criterion, the NNs approximation and the MLP (minimum learning parameter) technique. In the scheme, the nonlinear observeris first designed to estimate the unmeasurable states and the assumption “linear growthof the nonlinear function is released. Furthermore, the uncertainty of the wholesystem (including the perturbation of time-varying delay) is lumped and compensatedby employing one RBF NNs (radial basis function neural networks). Though, only two weight-norm related parameters are required tobe updated online for the merit of the MLP technique. And the gain-inversion relatedadaptive law is targetly designed to mitigate the adverse effect of unknown dead zone.Comparing with the previous work, the proposed algorithm obtains the advantage: a conciseform and easy to implementation due to its less computational burden. The theoretical analysis andcomparison example demonstrate the substantial effectiveness of the proposed scheme.


Acknowledgment

This work was supported by National Postdoctoral Program for Innovative Talents (Grant No. BX201600103), China Postdoctoral Science Foundation (Grant No. 2016M601600), National Natural Science Foundation of China (Grant Nos. 61473183, U1509211), and Fundamental Research Funds for the Central University (Grant No. 3132016001).


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