Distributed adaptive finite-time approach for formation-containment control of networked nonlinear systems under directed topology

  • Yujuan WANG
  • , Yongduan SONG*
  • , Wei REN
  • *Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

111 Citations (Scopus)

Abstract

This paper presents a distributed adaptive finite-time control solution to the formation-containment problem for multiple networked systems with uncertain nonlinear dynamics and directed communication constraints. By integrating the special topology feature of the new constructed symmetrical matrix, the technical difficulty in finite-time formation-containment control arising from the asymmetrical Laplacian matrix under single-way directed communication is circumvented. Based upon fractional power feedback of the local error, an adaptive distributed control scheme is established to drive the leaders into the prespecified formation configuration in finite time. Meanwhile, a distributed adaptive control scheme, independent of the unavailable inputs of the leaders, is designed to keep the followers within a bounded distance from the moving leaders and then to make the followers enter the convex hull shaped by the formation of the leaders in finite time. The effectiveness of the proposed control scheme is confirmed by the simulation.
Original languageEnglish
Pages (from-to)3164-3175
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume29
Issue number7
Early online date6 Jul 2017
DOIs
Publication statusPublished - Jul 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2012 IEEE.

Funding

This work was supported in part by technology transformation program of Chongqing higher education university (KJZH17102), National Science Foundation under GrantECCS-1307678 and National Natural Science Foundation of China under Grant 61528301.

Keywords

  • Directed interaction topology
  • finite-time control
  • formation-containment
  • nonlinear multiagent systems (MAS)

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