Batch-to-batch iterative learning control using linearised models with adaptive model updating

This paper presents batch-to-batch iterative learning control (ILC) of a fed-batch fermentation process using batch-wise linearised models identified from process operation data. The newly obtained process operation data after each batch is added to the historical data base and an updated linearised model is adaptively identified. In an effort to adapt to the current process environment, the updated model is identified from a moving window of the most recent historical batches. The new model is used to compute control policy for the next batch. The control policy at different batch stages are generally correlated as the overall control policy is obtained to maximize the amount of product at the end of a batch. To address the colinearity issue, partial least square (PLS) is used in estimating the linearised model parameters. The proposed strategy is applied to a simulated fed-batch fermentation process and the performance is evaluated. The effect of window sizes was studied. Simulation results show that the proposed approach improves the batch-to-batch ILC performance.