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题名:
Data-driven process decomposition and robust online distributed modelling for large-scale processes
作者: Zhang, Shu; Li LJ(李丽娟); Yao LJ(姚莉娟); Yang SP(杨世品); Zou T(邹涛)
作者部门: 工业控制网络与系统研究室
通讯作者: Li LJ(李丽娟)
关键词: Canonical correlation analysis ; affinity propagation clustering ; block-wise RPLS ; model reduction ; model-predictive control ; process control ; parameter identification
刊名: International Journal of Systems Science
ISSN号: 0020-7721
出版日期: 2018
卷号: 49, 期号:3, 页码:449-463
收录类别: SCI ; EI
EI收录号: 20175104554528
WOS记录号: WOS:000428635000001
产权排序: 2
项目资助者: National Natural Science Foundation of China [grant number 61203072], [grant number 61403190], [grant number 61773366] ; Research Innovation Program for College Graduates of Jiangsu Province [grant number KYLX16_0598].
摘要: With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.
语种: 英语
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/21466
Appears in Collections:工业控制网络与系统研究室_期刊论文

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作者单位: 1.Industrial System and Automation Department, College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, China
2.Industrial Control Networks and Systems Department, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China

Recommended Citation:
Zhang, Shu,Li LJ,Yao LJ,et al. Data-driven process decomposition and robust online distributed modelling for large-scale processes[J]. International Journal of Systems Science,2018,49(3):449-463.
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