Stabilizing switching control for nonlinear system based on quasi-ARX RBFN model

Lan Wang, Yu Cheng, Jinglu Hu

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

In this paper, a fuzzy switching adaptive control approach is presented for nonlinear systems. The proposed fuzzy switching adaptive control law is composed of a quasi-ARX radial basis function network (RBFN) prediction model and a fuzzy switching mechanism. The quasi-ARX RBFN prediction model consists of two parts: a linear part used for a linear controller to ensure boundedness of the input and output signals; and an RBFN nonlinear part used to improve control accuracy. By using the fuzzy switching scheme between the linear and nonlinear controllers to replace the 0/1 switching, it can realize a better balance between stability and accuracy. Theoretical analysis and simulation results show the effectiveness of the proposed control method on the stability, accuracy, and robustness.

Original languageEnglish
Pages (from-to)390-396
Number of pages7
JournalIEEJ Transactions on Electrical and Electronic Engineering
Volume7
Issue number4
DOIs
Publication statusPublished - 2012 Jul

Keywords

  • Fuzzy switching mechanism
  • Quasi-ARX prediction model
  • Radial basis function network
  • Stabilizing switching control

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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