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Batch-to-batch control of fed-batch processes using control-affine feedforward neural network

Lookup NU author(s): Dr Jie ZhangORCiD


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A control strategy for fed-batch processes is proposed based on control affine feed-forward neural network (CAFNN). Many fed-batch processes can be considered as a class of control affine nonlinear systems. CAFNN is constructed by a special structure to fit the control affine system. It is similar to a multi-layer feed-forward neural network, but it has its own particular feature to model the fed-batch process. CAFNN can be trained by a modified Levenberg-Marquardt (LM) algorithm. However, due to model-plant mismatches and unknown disturbances, the optimal control policy calculated based on the CAFNN model may not be optimal when applied to the fed-batch process. In terms of the repetitive nature of fed-batch processes, iterative learning control (ILC) can be used to improve the process performance from batch to batch. Due to the special structure of CAFNN, the gradient information of CAFNN can be computed analytically and applied to the batch-to-batch ILC. Under the ILC strategy from batch to batch, endpoint product qualities of fed-batch processes can be improved gradually. The proposed control scheme is illustrated on a simulated fed-batch ethanol fermentation process. © 2007 Springer-Verlag London Limited.

Publication metadata

Author(s): Xiong Z, Xu Y, Zhang J, Dong J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Neural Computing and Applications: 4th International Symposium on Neural Networks

Year of Conference: 2008

Pages: 425-432

ISSN: 0941-0643

Publisher: Springer


DOI: 10.1007/s00521-007-0142-6

Library holdings: Search Newcastle University Library for this item

ISBN: 14333058