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Modelling and optimal control of fed-batch processes using a novel control affine feedforward neural network

Lookup NU author(s): Dr Zhihua Xiong, Dr Jie ZhangORCiD

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Abstract

Many fed-batch processes can be considered as a class of control affine nonlinear systems. In this paper, a new type of neural network for modelling fed-batch processes, called as control affine feedforward neural network (CAFNN), is proposed. For constrained nonlinear optimal control of fed-batch processes, CAFNN offers an effective and simple optimal control strategy by sequential quadratic programming (SQP) where the gradient information can be computed directly from CAFNN. Thus the nonlinear programming problem can then be solved more accurately and efficiently. The proposed modelling and optimal control scheme are illustrated on a nonlinear system and a simulated fed-batch ethanol fermentation process. © 2003 Elsevier B.V. All rights reserved.


Publication metadata

Author(s): Xiong Z, Zhang J

Publication type: Article

Publication status: Published

Journal: Neurocomputing

Year: 2004

Volume: 61

Issue: 1-4

Pages: 317-337

ISSN (print): 0925-2312

ISSN (electronic): 1872-8286

Publisher: Elsevier BV

URL: http://dx.doi.org/10.1016/j.neucom.2003.11.006

DOI: 10.1016/j.neucom.2003.11.006


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