Stable adaptive control with recurrent neural networks for square MIMO non-linear systems

Authors:
Salem Zerkaoui;Fabrice Druaux;Edouard Leclercq;Dimitri Lefebvre
Affiliations:
Université Le Havre-GREAH, 25 rue P. Lebon, 76063 Le Havre, France;Université Le Havre-GREAH, 25 rue P. Lebon, 76063 Le Havre, France;Université Le Havre-GREAH, 25 rue P. Lebon, 76063 Le Havre, France;Université Le Havre-GREAH, 25 rue P. Lebon, 76063 Le Havre, France and IUT, Place Robert Schuman, BP 4006, 76610 le havre, France
Venue:
Engineering Applications of Artificial Intelligence
Year:
2009

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Abstract

In this paper, stable indirect adaptive control with recurrent neural networks is presented for square multivariable non-linear plants with unknown dynamics. The control scheme is made of an adaptive instantaneous neural model, a neural controller based on fully connected ''Real-Time Recurrent Learning'' (RTRL) networks and an online parameters updating law. Closed-loop performances as well as sufficient conditions for asymptotic stability are derived from the Lyapunov approach according to the adaptive updating rate parameter. Robustness is also considered in terms of sensor noise and model uncertainties. The control scheme is then applied to the Tennessee Eastman Challenge Process in order to illustrate the efficiency of the proposed method for real-world control problems.