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The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

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  • Xiaoyan Qin

Abstract

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

Suggested Citation

  • Xiaoyan Qin, 2018. "The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, September.
    • Handle: RePEc:hin:jnlmpe:4620125
    DOI: 10.1155/2018/4620125
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