Resumen

BARUCH, Ieroham  y  OLIVARES GUZMAN, José Luis. Implementation of a Neural Hierarchical Multimodel for Identification and Control of Mechanical Systems. Comp. y Sist. [online]. 2005, vol.9, n.1, pp.28-40. ISSN 2007-9737.

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.

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