An experimental study of neural feedforward controller with generalized disturbance error learning

Abstract

To overcome the limitation of linear feedfoforward controllers in nonlinear chemical processes, Lee and Park proposed the feedforward control scheme using a neural network and a dynamic matrix control (DMC). The neural feedforward controller (NFFC) using a neural network and a general linear controller is proposed to extend Lee and Park's control scheme. The generalized disturbance error learning method using the pseudo desired output is also proposed to train the neural network in the NFFC. Results are given for the implementation of the NFFC on a pilot-scale distillation column. The control performance of the NFFC is compared with conventional linear feedforward-feedback and feedback-only controllers such as the PI and the DMC controllers through various experiments. The trained NFFC shows excellent control performance compared with the linear controllers. The results indicate that the NFFC copes well with high nonlinearities and interactions, and may by useful in practice.