Implementación de un Multimodelo Neuronal Jerárquico para Identificación y Control de Sistemas Mecánicos

Implementation of a Neural Hierarchical Multimodel for Identification and Control of Mechanical Systems

Ieroham Baruch y José Luis Olivares Guzmán

CINVESTAV–IPN Departamento de Control Automático, Av. IPN 2508 Col. San Pedro Zacatenco, A.P. 14–740 C.P. 07360, México D.F., México baruch@ctrl.cinvestav.mx ; jolivares@ctrl.cinvestav.mx

Artículo recibido en octubre 17, 2003
Aceptado en abril 01, 2005

Resumen

En este artículo se propone la implementación de un Multimodelo Neuronal Jerárquico (MNJ) basándose en la símilarridad con el modelo difuso de Takagi–Sugeno. El modelo MNJ tiene tres partes: 1) fuzificación; 2) inferencia en el nivel bajo usando Redes Neuronales Recurrentes, RNR; 3) defuzifición en el nivel jerárquico alto usando una RNR que es en realidad un filtro–sumador ponderado de las salidas de las RNR del nivel bajo. El aprendizaje y el funcionamiento de ambos niveles jerárquicos son independientes. El modelo MNJ es implementado como identificador y controlador (feedforward, y feedback) en dos esquemas de control directo adaptable. Ambos esquemas de control son aplicados con una planta mecánica con fricción y comparados con otros esquemas de control neuronal y difuso, mostrando mejores resultados.

Palabras Clave: Control adaptable neuronal con modelo inverso, control neuronal directo adaptable, identificación de sistemas, Multimodelo Neuronal Jerárquico, Red Neuronal Recurrente Entrenable, sistema mecánico con fricción.

Abstract

The present paper proposed to implement a Neural Hierarchical Multi–Model (MNJ) based on the similarity with the fuzzy model of Takagi–Sugeno. The MNJ has three parts: 1) fuzzyfication; 2) inference engine in the lower hierarchical level, using Recurrent Neural Networks, RNR; 3) defuzzyfication in the upper hierarchical level, using one RNR doing a filtered weighted summation of the outputs of the lower level RNRs. The learning and functioning of both hierarchical levels is independent. The MNJ is implemented in two schemes of direct adaptive control as an identifier and as a feedforward/feedback controller, as well. Both control schemes are applied for control of a mechanical plant with friction and compared with other neural and fuzzy control schemes, exhibiting better results.

Keywords: Inverse model adaptive neural control, direct adaptive neural control, systems identification, Neural Hierarchical Multimodel, Recurrent Trainable Neural Network, mechanical system with friction.

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