文献类型：期刊文献

英文题名：Iterative learning control and it's application to batch process optimization

作者：Song, J.-R.[1]; Wang, H.-W.[2]; Shi, H.-B.[1]; Zhang, Sh.-H.[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] GuiZhou Space Appliance Co., Ltd., Shanghai Branch Office[R and D Center], Shanghai, China

年份：2011

外文期刊名：Proceedings - PACCS 2011: 2011 3rd Pacific-Asia Conference on Circuits, Communications and System

收录：EI(收录号：20113814345290)

语种：英文

外文关键词：Learning algorithms - Process control - Two term control systems - Batch data processing - Optimization - Iterative methods

摘要：An iterative learning control (ILC) algorithm based on recurrent wavelet neural network(RWNN) is proposed to control product final quality in batch process. recurrent Wavelet neural network is used to modeling long range batch process model. Due to model-plant mismatches and unmeasured disturbances, the calculated control policy based on RWNN model may not be optimal when applied to the actual process. By utilizing the repetitive nature of batch process , ILC is used to improve product final quality from batch to batch. Prediction models are modified based on previous prediction model average errors. Model errors are gradually reduced from batch to batch, control inputs approach to optimal control policy. The effectiveness is verified on a simulated batch process. ? 2011 IEEE.