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Iterative Learning Control of Batch Processes based on Time Varying Perturbation Models

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.

Publication metadata

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

Publication type: Article

Publication status: Published

Journal: Journal of Tsinghua University

Year: 2008

Volume: 48

Issue: S2

Pages: 1771-1774

ISSN (print): 0577-9189

ISSN (electronic): 1007-0214

Publisher: Qinghua Daxue, Xuebao Bianjibu