Iterative learning control-based batch process control technique for integrated control of end product properties and transient profiles of process variables

Cited 87 time in webofscience Cited 0 time in scopus
  • Hit : 361
  • Download : 0
Importance of batch processes has grown recently with the increasing economic competition that has pushed the manufacturing industries to pursue small quantity production of diverse high value-added products. Accordingly, systems engineering research on advanced control and optimization of batch processes has proliferated. In this paper, we examine the potentials of 'iterative learning control (ILC)' Lis a framework for industrial batch process control and optimization. First, various ILC rules are reviewed to provide a historical perspective. Next it is shown how the concept of ILC can be fused with model predictive control (MPC) to build an integrated end product and transient profile control technique for industrial chemical batch processes. Possible extensions and modifications of the technique are also presented along with some numerical illustrations. Finally, other related techniques are introduced to note the similarities and contemplate the opportunities for synergistic integration with the current ILC framework. (C) 2002 Elsevier Ltd. All rights reserved.
Publisher
ELSEVIER SCI LTD
Issue Date
2003-10
Language
English
Article Type
Article; Proceedings Paper
Keywords

MODEL-PREDICTIVE CONTROL; REPETITIVE CONTROL; QUALITY; SYSTEMS; CONVERGENCE; ALGORITHM

Citation

JOURNAL OF PROCESS CONTROL, v.13, no.7, pp.607 - 621

ISSN
0959-1524
DOI
10.1016/S0959-1524(02)00096-3
URI
http://hdl.handle.net/10203/79438
Appears in Collection
CBE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 87 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0