Indirect neural control for plant-wide systems: Application to the Tennessee Eastman Challenge Process

Abstract An autonomous indirect scheme is proposed for multivariable process control and is extended to unstable open-loop plant-wide processes. Our principal objective in this work is to prove the feasibility to control an industrial plant by a small size neural system without any a priori training. The control scheme is made of an adaptive instantaneous neural model, a Neural Controller based on fully connected “Real-Time Recurrent Learning” networks and an on-line parameters updating law. This control scheme is applied to the Tennessee Eastman Challenge Process. Performances such as set point stabilisation, mode switching and disturbances rejection are pointed out. Results are discussed according to the Down and Vogel control objectives.

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