Chaos control and synchronization, with input saturation, via recurrent neural networks

Authors:
Edgar N. Sanchez;Luis J. Ricalde
Affiliations:
CINVESTAV, Unidad Guadalajara, Apartado Postal 31-438, Plaza La Luna, Guadalajara, Jalisco C.P. 45091, Mexico;CINVESTAV, Unidad Guadalajara, Apartado Postal 31-438, Plaza La Luna, Guadalajara, Jalisco C.P. 45091, Mexico
Venue:
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Year:
2003

Citing 6
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Abstract

This paper deals with the adaptive tracking problem of non-linear systems in presence of unknown parameters, unmodelled dynamics and input saturation. A high order recurrent neural network is used in order to identify the unknown system and a learning law is obtained using the Lyapunov methodology. Then a stabilizing control law for the reference tracking error dynamics is developed using the Lyapunov methodology and the Sontag control law. Tracking error boundedness is established as a function of a design parameter. The new approach is illustrated by examples of complex dynamical systems: chaos control and synchronization.