Reliable Batch-to-Batch Iterative Learning Control of a Fed-batch Fermentation Process

Abstract

Abstract A reliable batch-to-batch iterative learning control (ILC) strategy is proposed. A batch-wise linearised model of the batch process is obtained from historical process operation data and is used to derive the ILC strategy. In order to enhance the reliability of ILC, model prediction confidence is incorporated in the ILC optimisation objective function and control policies leading to wide model prediction confidence bounds are penalised. In order to cope with nonlinearities, the batch-wise linearised model is re-identified after each batch run with the immediate previous batch as the reference batch. The proposed method is applied to a simulated fed-batch fermentation process and the results demonstrate that the proposed reliable ILC strategy is very effective.