Iterative learning control of a reactive polymer composite moulding process

This paper presents an iterative learning control strategy for a reactive polymer composite moulding process using batch-wise linearised models that are identified from process operational data. In rapid thermal moulding processes, the degree of cure is controlled by the applied mould temperature. Due to the model plant mismatches and the presence of unknown disturbances, a pre-set (off-line optimised) moulding temperature profile would not always lead to the desired degree of cure. The repetitive nature of batch moulding process allow the information of the previous batch (or cycle) being used to enhance the operation of the current batch through iterative learning control. The control policy updating is calculated by solving an optimisation problem using a model linearised around a reference batch. In order to cope with process nonlinearities, process variations, and disturbances, the reference batch is taken as the immediate previous batch and the model is re-identified from the updated historical batch data using principal component regression. The proposed method is tested on a simulated reactive polymer composite moulding process. (C) 2012 Published by Elsevier Ltd.