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Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model

Lookup NU author(s): Dr Zhihua Xiong, Dr Jie ZhangORCiD

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Abstract

A batch-to-batch optimal iterative learning control (ILC) strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation (LTVP) model is built for product quality around the nominal trajectories. To address problems of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. Then tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained. A rigorous theorem is proposed, to prove the convergence of tracking error under ILC. The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC. © 2008 Chemical Industry and Engineering Society of China (CIESC) and Chemical Industry Press (CIP).

Publication metadata

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

Publication type: Article

Publication status: Published

Journal: Chinese Journal of Chemical Engineering

Year: 2008

Volume: 16

Issue: 2

Pages: 235-240

Print publication date: 01/04/2008

ISSN (print): 1004-9541

ISSN (electronic): 0892-0370

Publisher: Chemical Industry Press

URL: http://dx.doi.org/10.1016/S1004-9541(08)60069-5

DOI: 10.1016/S1004-9541(08)60069-5

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