Identificación de Sistema Péndulo Invertido usando Redes Neuronales Multicapa y Polinomiales (Identification Inverted Pendulum System using Multilayer and Polynomial Neural Networks)

Luis Mario Lizárraga Orozco (lmlizarraga@upsin.edu.mx)1, Guillermo Ronquillo Lomeli (gronquillo@cidesi.mx)2, José Gabriel Ríos Moreno (riosg@uaq.mx)3, Mario Trejo Perea (mtp@uaq.mx)3


1Universidad Politécnica de Sinaloa
2Centro de Ingeniería y Desarrollo Industrial
3Universidad Autónoma de Querétaro

This paper appears in: Revista IEEE América Latina

Publication Date: May 2015
Volume: 13,   Issue: 5 
ISSN: 1548-0992


Abstract:
It is well known that the inverted pendulum can describe a variety of inherently unstable systems, which is a major reason to consider it as a benchmark problem in control and identification. In this paper, a comparison between two different kinds of neural networks is presented, on one hand the feed-forward multilayer network with back-propagation learning method, and in the other hand the Volterra polynomial basis function network. A Fuzzy Logic controller was implemented to stabilize the system around its operation point. Both neural networks were trained using the error between the model's output and the plant's actual output. The polynomial network shows better performance against the multilayer network

Index Terms:
Volterra polynomials, Basis function, Inverted pendulum, Nonlinear system, Identification, Neural Networks,   


Documents that cite this document
This function is not implemented yet.


[PDF Full-Text (1104)]