Recurrent Neuro-Fuzzy Modeling and Fuzzy MDPP Control for Flexible Servomechanisms

This paper considers the nonlinear system identification and control for flexible servomechanisms. A multi-step-ahead recurrent neuro-fuzzy model consisting of local linear ARMA (autoregressive moving average) models with bias terms is suggested for approximating the dynamic behavior of a servomecha...

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Bibliographic Details
Published in:Journal of intelligent & robotic systems 2003-10, Vol.38 (2), p.213-235
Main Authors: Lin, Chorng-shyan, Yang, Tachung, Jou, Yeong-chau, Lin, Lih-chang
Format: Article
Language:English
Subjects:
Artificial neural networks
Construction
Dynamical systems
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Nonlinear dynamics
Servomechanisms
Studies
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Summary:This paper considers the nonlinear system identification and control for flexible servomechanisms. A multi-step-ahead recurrent neuro-fuzzy model consisting of local linear ARMA (autoregressive moving average) models with bias terms is suggested for approximating the dynamic behavior of a servomechanism including the effects of flexibility and friction. The RLS (recursive least squares) algorithm is adopted for obtaining the optimal consequent parameters of the rules. Within each fuzzy operating region, a local MDPP (minimum degree pole placement) control law with integral action can be constructed based on the estimated local model. Then a fuzzy controller composed of these local MDPP controls can be easily constructed for the servomechanism. The techniques are illustrated using computer simulations.
ISSN:0921-0296
1573-0409
DOI:10.1023/A:1027339220324