Batch Process Iterative Learning Control Based on Wavelet Recurrent Neural Network

An iterative learning control (ILC) algorithm based on wavelet recurrent neural network(WRNN) was proposed to control product final quality in batch process. Wavelet recurrent neural network was used to establish the model of long range batch process. Due to model errors and unmeasured disturbances, the calculated control policy based on WRNN model may not be optimal when applied to the actual process. By utilizing the repetitive characteristic of batch process, ILC was used to improve product final quality from batch to batch. According to the mean value of previous prediction errors, the prediction output of neural network model was modified and the control input was computed for the next batch. And then, the model errors were gradually reduced from batch to batch, and the control inputs attained the optimal control policy. The effectiveness is verified via a simulated batch process.