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Batch process modelling and optimal control based on neural network models

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.

Publication metadata

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