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Batch to Batch Iterative Learning Control of A Fed-batch Fermentation Process Using Linearised Models

Lookup NU author(s): Dr Jie ZhangORCiD, Emeritus Professor Julian Morris


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This paper presents an iterative learning control strategy for a fed-batch fermentation process using linearised models identified from process operational data. Off-line calculated control policies for batch fermentation processes may not be optimal when implemented on the processes due to model plant mismatches and/or the presence of unknown disturbances. In order to overcome the effect of model plant mismatches and unknown disturbances, a batch to batch iterative learning control strategy is developed to modify the control actions for the next batch using the information obtained form current and previous batches. The control policy updating is calculated using a model linearised around a reference batch. In order to cope with process variations and disturbances, the reference batch can be taken as the immediate previous batch. After each batch, the newly obtained process operation data is added to the historical process data base and an updated linearised model is re-identified. Since the control actions during different stages of a batch are usually correlated, it is proposed in this paper that the linearised model can be identified from partial least square regression. The proposed technique has been successfully applied to a simulated fed-batch fermentation process.

Publication metadata

Author(s): Zhang J, Nguyan J, Morris J, Xiong ZH

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 10th International Conference on Control Automation Robotics & Vision (Icarcv 2008)

Year of Conference: 2008

Number of Volumes: 4

Pages: 745-750

Publisher: IEEE


DOI: 10.1109/ICARCV.2008.4795610

Library holdings: Search Newcastle University Library for this item

ISBN: 9781424422869