Please use this identifier to cite or link to this item: https://doi.org/10.3182/20120710-4-SG-2026.00050
Title: Data-driven based integrated learning controller design for batch processes
Authors: Jia, L.
Cao, L.
Chiu, M. 
Keywords: Batch process
Feedback control
Integrated learning control
Iterative learning control (ILC)
Issue Date: 2012
Source: Jia, L.,Cao, L.,Chiu, M. (2012). Data-driven based integrated learning controller design for batch processes. IFAC Proceedings Volumes (IFAC-PapersOnline) 8 (PART 1) : 234-238. ScholarBank@NUS Repository. https://doi.org/10.3182/20120710-4-SG-2026.00050
Abstract: The challenge of optimization control of batch processes is how to combine both discrete-time (batch-axis) information and continuous-time (time-axis) information into an integrated frame when designing optimal controller. By using data-driven technology, a novel integrated learning control system is proposed in this paper. Firstly, an iterative learning controller (ILC) is designed along the direction of batch-axis, and then an adaptive single neuron predictive controller (SNPC) that plays role of feedback controller along the direction of time-axis is devised accordingly. As a result, the integrated control system is very effective to eliminate modeling error and uncertainty, which is superior to traditional ILC. In addition, the self-tuning algorithm of SNPC controller is derived by a rigorous analysis based on the Lyapunov method such that the predicted tracking error convergences asymptotically. Lastly, to verify the efficiency of the proposed control scheme, it is applied to a benchmark batch process. The simulation results show that the proposed method has better stability and robustness compared with the traditional iterative learning control. © 2012 IFAC.
Source Title: IFAC Proceedings Volumes (IFAC-PapersOnline)
URI: http://scholarbank.nus.edu.sg/handle/10635/74532
ISBN: 9783902823052
ISSN: 14746670
DOI: 10.3182/20120710-4-SG-2026.00050
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