Neural network based iterative learning control for product qualities in batch processes

Abstract

A neural network based iterative learning control (NN-ILC) strategy is proposed to improve the product qualities in batch processes. Based on the repetitive nature of batch processes, iterative learning control (ILC) is used to improve product qualities gradually from batch to batch. The learning gain in the ILC is usually determined according to a linearised model. Instead of building a model for the system dynamics, a feed-forward neural network (FNN) is used directly as a non-linear learning gain in the ILC law. The tracking error profile of the previous batch is used as the input of the FNN, while the output of the network is the control change profile for the next batch run. It has been proved that if the network is trained properly based on the historical operation data, the tracking error under the proposed NN-ILC can converge to zero gradually with respect to the batch number. The neural network can also be retrained during the ILC to renew the learning gain in order to handle model uncertainties of the batch processes. The proposed control strategy is illustrated on a typical batch reactor.