Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/54617
Title: A novel iterative learning control for product quality control in batch process
Authors: Jia, L.
Shi, J.
Chiu, M. 
Keywords: Batch process
Fuzzy neural network
Iterative learning control
Product quality control
Issue Date: Aug-2009
Source: Jia, L.,Shi, J.,Chiu, M. (2009-08). A novel iterative learning control for product quality control in batch process. Huagong Xuebao/CIESC Journal 60 (8) : 2017-2023. ScholarBank@NUS Repository.
Abstract: Considering that it is difficult to analyze the convergence of iterative learning optimal control for quality control of batch processes, a novel iterative learning control based on data-driven neural fuzzy model for product quality control in batch process was proposed. In the presented algorithm, the region of the searching space for optimal solution is changed by adding a new constraint condition, which resulted in the convergence of the product quality in batch axes. Moreover, the rigorous proof was given. Lastly, to verify the efficiency of the proposed algorithm, it was applied to a benchmark batch process. The simulation results showed that the proposed method was better and could be applied to practical processes, thus it provides a new way for the control of batch processes. © All Rights Reserved.
Source Title: Huagong Xuebao/CIESC Journal
URI: http://scholarbank.nus.edu.sg/handle/10635/54617
ISSN: 04381157
Appears in Collections:Staff Publications

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