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Complexity
Volume 2017, Article ID 3125073, 11 pages
https://doi.org/10.1155/2017/3125073
Research Article

Feedforward Nonlinear Control Using Neural Gas Network

Departamento de Ingeniería Eléctrica, Electrónica de Computadores y Sistemas, Universidad de Oviedo, Edificio Departamental 2, Zona Oeste, Campus de Viesques s/n, 33204 Gijón/Xixón, Spain

Correspondence should be addressed to Iván Machón-González; se.ivoinu@navinohcam

Received 19 July 2016; Accepted 17 November 2016; Published 15 January 2017

Academic Editor: Francisco Gordillo

Copyright © 2017 Iván Machón-González and Hilario López-García. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the plant constitutes a piece-wise linear approximation of the nonlinear system and each neuron represents a local linear model for which a linear controller is designed. The neural gas model works as an observer and a controller at the same time. A state feedback control is implemented by estimation of the state variables based on the local transfer function that was provided by the local linear model. The gradient vectors obtained by the supervised neural gas algorithm provide a robust procedure for feedforward nonlinear control, that is, supposing the inexistence of disturbances.