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Data-driven process decomposition and robust online distributed modelling for large-scale processes
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
关键词Canonical Correlation Analysis Affinity Propagation Clustering Block-wise Rpls Model Reduction Model-predictive Control Process Control Parameter Identification
发表期刊International Journal of Systems Science
ISSN0020-7721
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
资助机构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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文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/21466
专题工业控制网络与系统研究室
通讯作者Li LJ(李丽娟)
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
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GB/T 7714
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.
APA Zhang, Shu,Li LJ,Yao LJ,Yang SP,&Zou T.(2018).Data-driven process decomposition and robust online distributed modelling for large-scale processes.International Journal of Systems Science,49(3),449-463.
MLA Zhang, Shu,et al."Data-driven process decomposition and robust online distributed modelling for large-scale processes".International Journal of Systems Science 49.3(2018):449-463.
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