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Lookup NU author(s): Dr Zhihua Xiong, Dr Jie ZhangORCiD
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A run-to-run model-based iterative learning control (ILC) strategy for the tracking control of product quality in batch processes is proposed. A linear perturbation model for product quality, linearized around the nominal trajectories, is identified from process operating data using linear regression. To address the problem of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. On the basis of the modified predictions, an ILC law with direct error feedback can be explicitly obtained. The convergence of tracking error under ILC is analyzed. To overcome the detrimental effects of unmeasured disturbances and process variations, it is proposed in this paper that the perturbation model should be updated in a batchwise manner. After the completion of each batch, a batchwise perturbation model, linearized around the control trajectory for that batch, is identified. A forgetting factor is introduced so that data from the more recent batch runs are weighted more than data from earlier batch runs. The proposed technique is successfully applied to a simulated batch reactor and a simulated batch polymerization process.
Author(s): Xiong Z, Zhang J
Publication type: Article
Publication status: Published
Journal: Industrial and Engineering Chemistry Research
Year: 2003
Volume: 42
Issue: 26
Pages: 6802-6814
ISSN (print): 0888-5885
ISSN (electronic):
Publisher: American Chemical Society
URL: http://dx.doi.org/10.1021/ie034006j
DOI: 10.1021/ie034006j
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