Simulation and experimental implementation of a neural-network-based internal-model control strategy on a reactor system

The use of inverse-model-based control strategy for nonlinear system has been increasing lately. However it is hampered by the difficulty in obtaining the inverse of nonlinear systems analytically. Since neural networks has the ability to model such inverses, it has become a viable alternative. Although many simulations using neural network inverse models for controls have been reported recently, no actual experimental application has been reported on a reactor system. In this paper we describe a novel experimental application of a neural network inverse-model based control method on a partially simulated pilot plant reactor, exhibiting steady stale parametric sensitivity and designed to test the use of such nonlinear algorithms. The implementation involved the control of the reactor temperature under set point changes, disturbance rejection and set point regulation with plant/model mismatches. Simulation tests on the model of the system were also carried out to enable better design of the neural network models and to highlight the differences between simulation and actual online results. The online implementation results obtained were sufficient to demonstrate the capability of applying these neural-network-based control methods in real systems