Control Neuronal por Modelo de Referencia para un Sistema de Estructura Variable
Model Reference Neural Control of a Variable Structure System
M. Margarita Goire C1, J. Martín Flores A.2, Moisés Bonilla3, Ieroham S. Baruch3
1 Facultad de ingeniería Eléctrica, Departamento de Informática
2 Secretaría de Marina, Armada de México ]]>
3 Centro de Investigación y Estudios Avanzados
Departamento de Control Automático
E–mails: mgoireie@fie.uo.edu.cu, cuenta02@prodigy.net.mx, mbonilla@enigma.red.cinvestav.mx, baruch@ctrl.cinvestav.mx
Artículo recibido en Junio 15, 2000; aceptado en Abril 10, 2003
Resumen
El objetivo de este trabajo es presentar una propuesta de control neuronal por modelo de referencia para un sistema que cambia su estructura interna de un sistema lineal de primer orden a un sistema lineal de segundo orden, aplicando para esta tarea una red neuronal recurrente. Se presentan dos esquemas de control neuronal por modelo de referencia para el sistema antes mencionado. Una de las características de la red neuronal que se utiliza es la de tener restricciones en sus pesos, esto garantiza su estabilidad durante el entrenamiento. En el primer esquema se utiliza una red neuronal para la identificación del sistema de estructura variable: en el segundo esquema se usan dos redes neuronales con el propósito de separar la identificación de cada subsistema.
Palabras Clave: Redes Neuronales, Sistemas de Estructura Variable, Control por Modelo de Referencia, Control Inteligente, Sistemas implícitos.
]]>Abstract
The objective of this paper is to propose a reference model neural control of a system, which change its internal structure from a linear system of first order to a linear system of second order, applying for this task a recurrent neural network. Two schemes of reference model neural control, for the above mentioned system, are presented. One characteristic feature of the neural network used, is that a feedback weight restriction is applied, which preserved its stability during the learning The first control scheme uses one neural network for identification of the variable structure system; the second control scheme uses two neural networks so to separate the identification of each subsystem.
Keywords: Neural Networks, Variable Structure Systems, Model Reference Control, Intelligent Control, Implicit Systems.
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