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

Lookup NU author(s): Dr Jie ZhangORCiD


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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.

Publication metadata

Author(s): Xiong ZH, Dong J, Zhang J

Publication type: Article

Publication status: Published

Journal: Science in China Series F: Information Sciences

Year: 2009

Volume: 52

Issue: 7

Pages: 1136-1144

Print publication date: 17/07/2009

ISSN (print): 1009-2757

ISSN (electronic): 1862-2836

Publisher: Science Press


DOI: 10.1007/s11432-009-0123-8


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Funder referenceFunder name
IBM China Research Lab 2008 UR-Program
2007AA041402National High-Tech Research & Development Program of China
2006A62New Star of Science and Technology of Beijing City
60404012National Natural Science Foundation of China
60874049National Natural Science Foundation of China