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Computación y Sistemas

On-line version ISSN 2007-9737Print version ISSN 1405-5546

Comp. y Sist. vol.14 n.2 Ciudad de México Oct./Dec. 2010

 

Artículos

 

Generación y optimización de controladores difusos utilizando el modelo NEFCON

 

Generation and Optimization of Fuzzy Controllers Using the NEFCON Model

 

Erik V. Cuevas Jiménez1,2, Daniel Zaldívar Navarro1,2, Marco Pérez Cisneros1 y Ernesto Tapia Rodríguez2

 

1 Departamento de Ciencias computacionales, Universidad de Guadalajara, CUCEI Av. Revolución 1500, Guadalajara, Jal, México. E–mail: erik.cuevas@cucei.udg.mx, daniel.zaldivar@cucei.udg.mx, marco.perez@cucei.udg.mx

2 Institut fur Informatik, Freie Universität Berlin Takustr. 9, Berlin, Alemania tapia@inf.fu–berlin.de

 

Artículo recibido en Enero 07, 2008.
Aceptado en Marzo 26, 2009.

 

Resumen

El diseño de algoritmos que operen sobre plantas con dinámicas no modeladas aún representa un reto en el área de control automático. Una solución podría ser el uso de algoritmos capaces de aprender en tiempo real mediante la interacción directa con la planta. El modelo NEFCON, permite construir la estructura de un controlador difuso del tipo Mamdani capaz de aprender las reglas y adaptar los conjuntos difusos. La principal ventaja del modelo NEFCON respecto a otros enfoques de aprendizaje, es que su diseño se reduce a expresar la calidad del error actual de la planta a controlar. Sin embargo, una desventaja del modelo NEFCON es la pobre exploración de los estados de la planta durante el aprendizaje, lo cual hace imposible su aplicación para sistemas dinámicos no lineales. En este trabajo se propone la adición de ruido Gaussiano a las variables de estado de la planta, con el objetivo de asegurar una exploración amplia de los estados, facilitando la convergencia del algoritmo de aprendizaje, cuando se aplica a sistemas no lineales. En particular, se muestra la efectividad de la propuesta en el control del sistema dinámico de la "pelota y el balancín" (Ball and Beam)

Palabras clave: Sistemas de control adaptativos, sistemas de control por aprendizaje, control inteligente, control no lineal.

 

Abstract

The design of algorithms that operate on un–modeled dynamics plants still represents a challenge in automatic control area. A solution could be the use of algorithms able to learn in real time by direct interaction with the plant. NEFCON, allows to build a Mamdani fuzzy controller able to learn rules and adapt the fuzzy sets. The main advantage of NEFCON compared with other learning approaches, is that its design express the current error state of the plant to be controlled. However, a disadvantage of NEFCON is its poor exploration of the states of the plant during the learning; disable its application on nonlinear dynamic systems. In this work the addition of Gaussian noise to the states of the plant is proposed with the objective to assure a wide exploration of the states, simplifying the convergence, when it is applied to nonlinear systems. In particular, the effectiveness of our proposal is shown in the control of the "ball and beam" dynamic system.

Keywords: Adaptive control systems, learning control systems, intelligent control, nonlinear control.

 

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