Optimal iterative learning control for end-point product qualities in semi-batch process based on neural network model  

作　　者：XlONG ZhiHua DONG Jin ZHANG Jie 

机构地区：[1]Department of Automation, Tsinghua University, Beijing 100084, China [2]Supply Chain Management & Logistics, IBM China Research Lab, Beijing 100094, China [3]School of Chemical Engineering and Advanced Materials, University of Newcastle, Newcastle upon Tyne, NE1 7RU, UK

出　　处：《Science in China(Series F)》2009年第7期1136-1144,共9页中国科学（F辑英文版）

基　　金：Supported by the National Natural Science Foundation of China (Grant Nos. 60404012, 60874049);the National High-Tech Research & Development Program of China (Grant No. 2007AA041402);the New Star of Science and Technology of Beijing City (Grant No. 2006A62);the IBM China Research Lab 2008 UR-Program

摘　　要：An optimal iterative learning control （ILC） strategy of improving endpoint products in semi-batch processes is presented by combining a neural network model. Control affine feed-forward neural network （CAFNN） is proposed to build a model of semi-batch process. The main advantage of CAFNN is to obtain analytically its gradient of endpoint products with respect to input. Therefore, an optimal ILC law with direct error feedback is obtained explicitly, and the convergence of tracking error can be analyzed theoretically. It has been proved that the tracking errors may converge to small values. The proposed modeling and control strategy is illustrated on a simulated isothermal semi-batch reactor, and the results show that the endpoint products can be improved gradually from batch to batch.An optimal iterative learning control （ILC） strategy of improving endpoint products in semi-batch processes is presented by combining a neural network model. Control affine feed-forward neural network （CAFNN） is proposed to build a model of semi-batch process. The main advantage of CAFNN is to obtain analytically its gradient of endpoint products with respect to input. Therefore, an optimal ILC law with direct error feedback is obtained explicitly, and the convergence of tracking error can be analyzed theoretically. It has been proved that the tracking errors may converge to small values. The proposed modeling and control strategy is illustrated on a simulated isothermal semi-batch reactor, and the results show that the endpoint products can be improved gradually from batch to batch.

关 键 词：iterative learning control neural network semi-batch process product quality 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP273.22[自动化与计算机技术—控制科学与工程]