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Lookup NU author(s): Dr Jie ZhangORCiD
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This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.
Author(s): Zhang J
Publication type: Article
Publication status: Published
Journal: Zidonghua Xuebao/Acta Automatica Sinica
Year: 2005
Volume: 31
Issue: 1
Pages: 19-31
Print publication date: 01/01/2005
ISSN (print): 0254-4156
ISSN (electronic): 1874-1029
Publisher: Kexue Chubanshe
URL: http://www.scopus.com/record/display.url?eid=2-s2.0-15244357925&origin=resultslist&sort=plf-f&src=s&st1=Batch+process+modelling+and+optimal+control+based+on+neural+network+models&sid=re52V44rfclc0GefVfRHeCa%3a30&sot=b&sdt=b&sl=89&s=TITLE-ABS-KEY%28Batch+process+modelling+and+optimal+control+based+on+neural+network+models%29&relpos=4&relpos=4
