Implementation of an Inverse-Model-Based Control Strategy Using Neural Networks on a Partially Simulated Exothermic Reactor

Recently, the use of control strategies based upon inverse process models for non-linear systems has been found promising. The requirement of a true analytical inverse can be avoided when neural network models are used; they have the ability to approximate both the forward and the inverse system dynamics. Although many simulation studies have illustrated the use of neural network inverse models for control, very few on-line applications have been reported. This paper describes a novel implementation of a neural network inverse-model based control method on a experimental system—a partially simulated reactor, designed to test the use of such non-linear algorithms. The implementation involved the control of the reactor temperature in the face of set point changes and load disturbances despite the existence of significant plant/model mismatch. Comparison was also made with conventional PID cascade control in several cases. The results obtained show the capability of these neural-network-based controllers and, incidentally, point out the differences between simulation studies and on-line experimental tests. Since the system in this study was only mildly non-linear, in some cases, the performance was comparable to that achieved by classical controllers while in other cases an improved control was achieved.