Please use this identifier to cite or link to this item: https://doi.org/10.1177/0142331211428234
Title: Neuro-fuzzy-based dynamic quadratic criterion-iterative learning control for batch process
Authors: Li, J.
Jiping, S.
Min-Sen, C. 
Keywords: Batch processes
iterative learning control
neuro-fuzzy model
quadratic criterion
Issue Date: Feb-2013
Source: Li, J., Jiping, S., Min-Sen, C. (2013-02). Neuro-fuzzy-based dynamic quadratic criterion-iterative learning control for batch process. Transactions of the Institute of Measurement and Control 35 (1) : 92-101. ScholarBank@NUS Repository. https://doi.org/10.1177/0142331211428234
Abstract: Considering the potentials of iterative learning control as a framework for industrial batch process control and optimization, a novel dynamic parameters-based quadratic criterion-iterative learning control (Q-ILC) is proposed in this paper. Firstly, Q-ILC with dynamic parameter is used to improve the performance of ILC. As a result, the proposed method can avoid the problem of initialization of the optimization controller parameters, in which a trial and error procedure is usually resorted to in the existing iterative algorithms used for the optimization of the batch process. Next, we make the first attempt to provide a rigorous description and proof to verify that the changes of the ILC policy converges with respect to the batch index number, which are normally validated only on the basis of the simulation results in the previous works. Lastly, an example is used to illustrate the performance and applicability of the proposed method. © The Author(s) 2011.
Source Title: Transactions of the Institute of Measurement and Control
URI: http://scholarbank.nus.edu.sg/handle/10635/74681
ISSN: 01423312
DOI: 10.1177/0142331211428234
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