A HIERARCHICAL FUZZY-NEURAL MULTI-MODEL - An application for a mechanical system with friccion identification and control

Ieroham Baruch, Jose Luis Olivares, Federico Thomas

2005

Abstract

A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture and a dynamic Backpropagation learning method are applied for identification and control of complex nonlinear mechanical plants. The paper uses a Fuzzy-Neural Hierarchical Multi-Model (FNHMM), which merge the fuzzy model flexibility with the learning abilities of the RNNs. The paper proposed the application of two control schemes, which are: a trajectory tracking control by an inverse FNHMM and a direct adaptive control, using the states issued by the identification FNHMM. The proposed control methods are applied for a mechanical plant with friction system control, where the obtained comparative results show that the control using FNHMM outperforms the fuzzy and the neural single control.

Paper Citation

in Harvard Style

Baruch I., Luis Olivares J. and Thomas F. (2005). A HIERARCHICAL FUZZY-NEURAL MULTI-MODEL - An application for a mechanical system with friccion identification and control . In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 972-8865-29-5, pages 230-235. DOI: 10.5220/0001174702300235

in Bibtex Style

@conference{icinco05,
author={Ieroham Baruch and Jose Luis Olivares and Federico Thomas},
title={A HIERARCHICAL FUZZY-NEURAL MULTI-MODEL - An application for a mechanical system with friccion identification and control},
booktitle={Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2005},
pages={230-235},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001174702300235},
isbn={972-8865-29-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - A HIERARCHICAL FUZZY-NEURAL MULTI-MODEL - An application for a mechanical system with friccion identification and control
SN - 972-8865-29-5
AU - Baruch I.
AU - Luis Olivares J.
AU - Thomas F.
PY - 2005
SP - 230
EP - 235
DO - 10.5220/0001174702300235