Paper
21 March 2001 Identification and control of nonlinear systems using neural networks with variable-structure-control-based learning algorithms
Francklin Rivas-Echeverria, Eliezer Colina-Morles, Iselba Mazzei-Rivas
Author Affiliations +
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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Francklin Rivas-Echeverria, Eliezer Colina-Morles, and Iselba Mazzei-Rivas "Identification and control of nonlinear systems using neural networks with variable-structure-control-based learning algorithms", Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); https://doi.org/10.1117/12.421177
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Nonlinear dynamics

Adaptive control

Systems modeling

Complex systems

Control systems

Neural networks

Neurons

Back to Top