Adaptive neural network control for a class of nonlinear systems

  • Chao Yang*
  • , Yingmin Jia
  • , Changqing Chen
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An adaptive neural network control scheme is developed for perturbed nonlinear systems with unknown functions. To avoid the curse of dimensionality, dynamic surface-control (DSC) technique is introduced in the progress of controller design. Moreover, the problem of singularity is solved in estimation of the unknown functions by designing a novel strategy of estimation. It is shown that the DSC-based controller can ensure semi-global uniform ultimate bounded of the closed-loop system, and the tracking error can be arbitrarily small with appropriate design parameters. A simulation example is used to demonstrate the validness of the proposed algorithm.

Original languageEnglish
Title of host publicationProceedings of 2016 Chinese Intelligent Systems Conference
EditorsWeicun Zhang, Yingmin Jia, Hongbo Li, Junping Du
PublisherSpringer Verlag
Pages143-151
Number of pages9
ISBN (Print)9789811023347
DOIs
StatePublished - 2016
EventInternational Conference on Chinese Intelligent Systems Conference, CISC 2016 - Xiamen, China
Duration: 1 Jan 2016 → …

Publication series

NameLecture Notes in Electrical Engineering
Volume405
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Chinese Intelligent Systems Conference, CISC 2016
Country/TerritoryChina
CityXiamen
Period1/01/16 → …

Keywords

  • Adaptive neural control
  • Dynamic surface-control
  • Nonlinear systems

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