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Direct adaptive neural control for strict-feedback stochastic nonlinear systems

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Abstract

This note considers the problem of direct adaptive neural control for a class of nonlinear single-input/single-output (SISO) strict-feedback stochastic systems. The variable separation technique is introduced to decompose the coefficient functions of the diffusion term. Radical basis function (RBF) neural networks are used to approximate unknown and desired control signals, then a novel direct adaptive neural controller is constructed via backstepping. The proposed adaptive neural controller guarantees that all the signals in the closed-loop system remain bounded in probability. A main advantage of the proposed controller is that it contains only one adaptive parameter needed to be updated online. Simulation results demonstrate the effectiveness of the proposed approach.

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Authors and Affiliations

  1. Institute of Complexity Science, Qingdao University, Qingdao, 266071, Shandong, P.R. China

    Huanqing Wang, Bing Chen & Chong Lin

  2. School of Mathematics and Physics, Bohai University, Jinzhou, 121000, Liaoning, P.R. China

    Huanqing Wang

Authors
  1. Huanqing Wang
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  2. Bing Chen
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  3. Chong Lin
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Corresponding author

Correspondence to Huanqing Wang.

Additional information

This work is partially supported by the Natural Science Foundation of China (61074008 and 61174033).

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Wang, H., Chen, B. & Lin, C. Direct adaptive neural control for strict-feedback stochastic nonlinear systems. Nonlinear Dyn 67, 2703–2718 (2012). https://doi.org/10.1007/s11071-011-0182-4

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  • DOI: https://doi.org/10.1007/s11071-011-0182-4

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