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

Jun Mao, Zhengrong Xiang, Guisheng Zhai, Jian Guo

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1679-1694
Number of pages16
JournalNonlinear Dynamics
Volume92
Issue number4
DOIs
Publication statusPublished - 2018 Jun 1

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Keywords

  • Adaptive practical stabilization
  • Dynamic signal
  • Neural networks
  • Nonlinear systems
  • Sampled-data control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanical Engineering
  • Applied Mathematics
  • Electrical and Electronic Engineering

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