Share Email Print

Proceedings Paper

Identification and control of nonlinear systems using neural networks with variable-structure-control-based learning algorithms
Author(s): Francklin Rivas-Echeverria; Eliezer Colina-Morles; Iselba Mazzei-Rivas
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 21 March 2001
PDF: 11 pages
Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); doi: 10.1117/12.421177
Show Author Affiliations
Francklin Rivas-Echeverria, Univ. de Los Andes (Venezuela)
Eliezer Colina-Morles, Univ. de Los Andes (Venezuela)
Iselba Mazzei-Rivas, Univ. de Los Andes (Venezuela)

Published in SPIE Proceedings Vol. 4390:
Applications and Science of Computational Intelligence IV
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?