Recurrent neural identification and an i-term direct adaptive control of nonlinear oscillatory plant

Authors:
Ieroham Baruch;Sergio M. Hernandez;Jacob Moreno-Cruz;Elena Gortcheva
Affiliations:
Department of Automatic Control, Cinvestav-Ipn, Mexico City, Mexico;Department of Automatic Control, Cinvestav-Ipn, Mexico City, Mexico;Department of Automatic Control, Cinvestav-Ipn, Mexico City, Mexico;Information & Technology Systems Department, Graduate School, University of Maryland University College, Adelphi, MD
Venue:
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
Year:
2012

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Abstract

A new Modular Recurrent Trainable Neural Network (MRTNN) has been used for system identification of two-mass-resort-damper nonlinear oscillatory plant. The first MRTNN module identified the exponential part of the unknown plant and the second one - the oscillatory part of the plant. The plant has been controlled by a direct adaptive neural control system with integral term. The RTNN controller used the estimated parameters and states to suppress the plant oscillations and the static plant output control error is reduced by an I-term added to the control.