Identification and control of nonlinear systems using neural networks with variable structure control-based learning algorithms

F. Rivas-Echeverría, E. Colina-Morles, I. Mazzei-Rivas

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper presents a Variable Structure Control (VSC)-based algorithm for adjusting a set of time varying parameters of virtual linear models that resemble linear dynamical neurons, used as on-line representations for a class of uncertain nonlinear processes. These virtual linear models allow the implementation of adaptive controllers in order to achieve predefined specifications for the closed-loop of the uncertain nonlinear process, or to force the tracking of the process output to reference models outputs accurately. A proof of the finite time convergence of the virtual linear model variables to the uncertain nonlinear process variables is included and some examples are contemplated to illustrate the proposed control design approaches.

Original languageEnglish (US)
Pages (from-to)252-262
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4390
DOIs
StatePublished - 2001
Externally publishedYes
EventApplications and Science of Computational Intelligence IV - Orlando, FL, United States
Duration: Apr 17 2001Apr 18 2001

Keywords

  • Adaptive control
  • Model reference adaptive control
  • Neural networks
  • Nonlinear systems
  • State feedback control
  • Variable structure control

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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