Batch-to-batch control of batch processes based on multilayer recurrent fuzzy neural network
Authors
He Liu1, Li Jia, Qing Liu, Dao Huang
1School of Information Science and Engineering, East China University of Science and Technology
Corresponding Author
He Liu
Available Online October 2007.
- DOI
- 10.2991/iske.2007.165How to use a DOI?
- Keywords
- Batch-to-batch control; Iterative learning control; Recurrent neural network; Batch process
- Abstract
The batch-to-batch model-based iterative optimal control strategy for batch processes is realized based on multilayer recurrent fuzzy neural network (MRFNN) and chaotic search. MRFNNs are used to model batch processes. Modeling and optimization problems are mainly solved by chaotic search. Due to model-plant mismatches and disturbances, the calculated optimal control profile may not be optimal when applied to the actual process. Current predictions are improved by prediction errors from previous batches, and the model errors are gradually reduced from batch-to-batch. Furthermore, the control strategy is developed for temperature tracking control. The effectiveness is verified on simulated batch reactors.
- Copyright
- © 2007, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - He Liu AU - Li Jia AU - Qing Liu AU - Dao Huang PY - 2007/10 DA - 2007/10 TI - Batch-to-batch control of batch processes based on multilayer recurrent fuzzy neural network BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 971 EP - 976 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.165 DO - 10.2991/iske.2007.165 ID - Liu2007/10 ER -