Consensus Maneuvering of Uncertain Nonlinear Strict-Feedback Systems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

  • Yibo Zhang
  • Dan Wang
  • Zhouhua Peng

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)139-146
Journal / PublicationLecture Notes in Computer Science
Volume10639
Publication statusPublished - Nov 2017

Conference

Title24th International Conference on Neural Information Processing (ICONIP 2017)
PlaceChina
CityGuangzhou
Period14 - 18 November 2017

Abstract

In this paper, a consensus maneuvering problem is investigated for uncertain nonlinear systems in strict-feedback form. Consensus maneuvering controllers are developed based on a modular design approach. Specifically, an estimation module is proposed, where a neural network is employed for approximating the unknown nonlinearities. Then, a controller module is designed based on a modified dynamic surface control method. Finally, the input-to-state stability of the close-loop system is analyzed via cascade theory, and the consensus maneuvering error is proved to converge to a residual set.

Research Area(s)

  • Consensus maneuvering, Modular design approach, Strict-Feedback system, Uncertain nonlinearity

Citation Format(s)

Consensus Maneuvering of Uncertain Nonlinear Strict-Feedback Systems. / Zhang, Yibo; Wang, Dan; Peng, Zhouhua.

In: Lecture Notes in Computer Science, Vol. 10639, 11.2017, p. 139-146.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal