Please use this identifier to cite or link to this item:
|Title:||Data-driven based integrated learning controller design for batch processes|
Integrated learning control
Iterative learning control (ILC)
|Citation:||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)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 19, 2019
checked on Oct 27, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.