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Lookup NU author(s): Dr Jie ZhangORCiD
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This paper presents an iterative learning control technique for batch processes based on time varying perturbation models. The linear perturbation models for product quality, linearized around the nominal trajectories, are identified from process operational data using principal component regression (PCR) and partial least squares (PLS) regression. In order 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. The perturbation model is updated in a batch-wise manner. After the completion of each batch, a batch-wise perturbation model, linearized around the control trajectory for that batch, is identified. PCR and PLS can overcome the correlations between the control actions during different stage of a batch and, hence, obtain accurate models. The proposed technique is successfully applied to a simulated fed-batch reactor.
Author(s): Zhang J, Nguyen J, Xiong Z
Publication type: Article
Publication status: Published
Journal: Journal of Tsinghua University
ISSN (print): 0577-9189
ISSN (electronic): 1007-0214
Publisher: Qinghua Daxue, Xuebao Bianjibu