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|Title:||Nonrestraint-iterative learning-based optimal control for batch processes||Authors:||Jia, L.
Product quality control
|Issue Date:||Aug-2010||Citation:||Jia, L.,Shi, J.,Chiu, M.-S.,Yu, J. (2010-08). Nonrestraint-iterative learning-based optimal control for batch processes. Huagong Xuebao/CIESC Journal 61 (8) : 1889-1893. 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 is proposed in this paper, which results in the convergence of the product quality and control trajectory in batch axes. Moreover, the rigorous proof is given. Lastly, to verify the efficiency of the proposed algorithm, it was applied to a benchmark batch process. The simulation results show that the proposed method is better and can 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/10635/64298||ISSN:||04381157|
|Appears in Collections:||Staff Publications|
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