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

被引：3
作者：
Xiong ZhiHua [1 ]
Dong Jin [2 ]
Zhang Jie [3 ]
机构：
[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
[2] IBM China Res Lab, Beijing 100094, Peoples R China
[3] Newcastle Univ, Sch Chem Engn & Adv Mat, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
来源：
SCIENCE IN CHINA SERIES F-INFORMATION SCIENCES | 2009年 / 52卷 / 07期
基金：
中国国家自然科学基金;
关键词：
iterative learning control; neural network; semi-batch process; product quality; OPTIMAL-CONTROL STRATEGY; FED-BATCH PROCESSES; OPTIMIZATION; SYSTEMS; REACTORS;
D O I：
10.1007/s11432-009-0123-8
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
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.
引用
收藏
页码：1136 / 1144
页数：9
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