Please use this identifier to cite or link to this item:
|Title:||Neuro-fuzzy-based dynamic quadratic criterion-iterative learning control for batch process|
iterative learning control
|Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 17, 2018
WEB OF SCIENCETM
checked on Sep 5, 2018
checked on Sep 7, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.