Distributed containment control for nonlinear multiagent systems in pure-feedback form

Guozeng Cui, Shengyuan Xu, Xinkai Chen, Frank L. Lewis, Baoyong Zhang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this paper, the problem of distributed containment control for pure-feedback nonlinear multiagent systems under a directed graph topology is investigated. The dynamics of each agent are molded by high-order nonaffine pure-feedback form. Neural networks are employed to identify unknown nonlinear functions, and dynamic surface control technique is used to avoid the problem of explosion of complexity inherent in backstepping design procedure. The Frobenius norm of the ideal neural network weighting matrices is estimated, which is helpful to reduce the number of the adaptive tuning law and alleviate the networked communication burden. The proposed distributed containment controllers guarantee that all signals in the closed-loop systems are cooperatively semiglobally uniformly ultimately bounded, and the outputs of followers are driven into a convex hull spanned by the multiple dynamic leaders. Finally, the effectiveness of the developed method is demonstrated by simulation examples.

Original languageEnglish
Pages (from-to)2742-2758
Number of pages17
JournalInternational Journal of Robust and Nonlinear Control
Volume28
Issue number7
DOIs
Publication statusPublished - 2018 May 10

Keywords

  • adaptive control
  • containment control
  • nonlinear multiagent systems
  • pure-feedback systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Chemical Engineering(all)
  • Biomedical Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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