Iterative learning control of a reactive polymer composite moulding process using batch-wise updated linearised models

This paper presents an iterative learning control strategy for a reactive polymer composite moulding process using linearised models identified from process operational data. The control actions for the next batch are modified using the information obtained from the current and previous batches. The control policy updating is calculated using a model linearised around a reference batch. In order to cope with process nonlinearities, process variations, and disturbances, the reference batch can be taken as the immediate previous batch. In such a way, the model is a batch-wise linearised model and is updated after each batch. The newly obtained process operation data after each batch is added to the historical data base and an updated linearised model is re-identified. Simulation results show that the iterative learning control strategy can improve the final degree of cure from batch to batch despite the presence of model plant mismatches and unknown disturbances.