Recurrent neural identification and i-term sliding mode control of a vehicle system using levenberg-marquardt learning

Authors:
Ieroham Baruch;Sergio-Miguel Hernandez-Manzano;Jacob Moreno-Cruz
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
Venue:
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Year:
2012

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Abstract

A new Modular Recurrent Trainable Neural Network (MRTNN) has been used for system identification of a vehicle motor system. The first MRTNN module identified the exponential part of the unknown vehicle motor plant and the second one - the oscillatory part of that plant. The vehicle motor plant has been controlled by an indirect sliding mode adaptive control system with integral term. The sliding mode controller used the estimated parameters and states to suppress the vehicle plant oscillations and the static plant output control error is reduced by an I-term added to the control.