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Observer-based adaptive neural dynamic surface control for a class of non-strict-feedback stochastic nonlinear systems

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  • Zhaoxu Yu
  • Shugang Li
  • Fangfei Li

Abstract

The problem of adaptive output feedback stabilisation is addressed for a more general class of non-strict-feedback stochastic nonlinear systems in this paper. The neural network (NN) approximation and the variable separation technique are utilised to deal with the unknown subsystem functions with the whole states. Based on the design of a simple input-driven observer, an adaptive NN output feedback controller which contains only one parameter to be updated is developed for such systems by using the dynamic surface control method. The proposed control scheme ensures that all signals in the closed-loop systems are bounded in probability and the error signals remain semi-globally uniformly ultimately bounded in fourth moment (or mean square). Two simulation examples are given to illustrate the effectiveness of the proposed control design.

Suggested Citation

  • Zhaoxu Yu & Shugang Li & Fangfei Li, 2016. "Observer-based adaptive neural dynamic surface control for a class of non-strict-feedback stochastic nonlinear systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(1), pages 194-208, January.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:1:p:194-208
    DOI: 10.1080/00207721.2015.1043364
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