Adaptive practical stabilization of a class of uncertain nonlinear systems via sampled-data control

Jun Mao, Zhengrong Xiang, Guisheng Zhai, Jian Guo

研究成果: Article査読

15 被引用数 (Scopus)

抄録

In this paper, an adaptive practical stabilization problem is investigated for a class of nonlinear systems via sampled-data control. The systems under study possess uncertain dynamics and unknown gain functions. During sampled-data controller design procedure, a dynamic signal is introduced to dominate the unmeasured states existed in the external disturbances, and neural networks are adopted to approximate the unknown nonlinear functions. By choosing appropriate sampling period, the designed sampled-data controller can render all states of the resulting closed-loop system to be semi-globally uniformly ultimately bounded. Two examples are given to demonstrate feasibility and efficacy of the proposed methods.

本文言語English
ページ(範囲)1679-1694
ページ数16
ジャーナルNonlinear Dynamics
92
4
DOI
出版ステータスPublished - 2018 6 1

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 航空宇宙工学
  • 海洋工学
  • 機械工学
  • 電子工学および電気工学
  • 応用数学

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