A process transfer model-based optimal compensation control strategy for batch process using just-in-time learning and trust region method

doi:10.1016/j.jfranklin.2020.10.039

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	A process transfer model-based optimal compensation control strategy for batch process using just-in-time learning and trust region method
	Chu, Fei 1,2,3; Cheng, Xiang 3; Peng, Chuang 1; Jia, Runda 4; Chen, Tao 5; Wei, Qinglai3
发表期刊	JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
ISSN	0016-0032
	2021
卷号	358 期号:1 页码:606-632
通讯作者	Chu, Fei(chufeizhufei@sina.com)
摘要	The advantages of maximally transferring similar process data for modeling make the process transfer model attract increasing attention in quality prediction and optimal control. Unfortunately, due to the difference between similar processes and the uncertainty of data-driven model, there are usually a more serious mismatch between the process transfer model and the actual process, which may result in the deterioration of process transfer model-based control strategies. In this research, a process transfer model based optimal compensation control strategy using just-in-time learning and trust region method is proposed to cope with this problem for batch processes. First, a novel JITL-JYKPLS (Just-in-time learning Joint-Y kernel partial least squares) model combining the JYKPLS (Joint-Y kernel partial least squares) process transfer model and just-in-time learning is proposed and employed to obtain the satisfactory approximation in a local region with the assistance of sufficient similar process data. Then, this paper integrates JITL-JYKPLS model with the trust region method to further compensate for the NCO (necessary condition of optimality) mismatch in the batch-to-batch optimization problem, and the problem of estimating experimental gradients is also avoided. Meanwhile, a more elaborate model update scheme is designed to supplement the lack of new data and gradually eliminate the adverse effects of partial differences between similar process production processes. Finally, the feasibility of the proposed optimal compensation control strategy is demonstrated through a simulated cobalt oxalate synthesis process. (C) 2020 Published by Elsevier Ltd on behalf of The Franklin Institute.
DOI	10.1016/j.jfranklin.2020.10.039
关键词[WOS]	ONLINE QUALITY PREDICTION ; SOFT SENSOR ; PRODUCT QUALITY ; OPTIMIZATION ; REGRESSION ; DIAGNOSIS ; TRACKING
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61973304] ; National Natural Science Foundation of China[61503384] ; National Natural Science Foundation of China[61873049] ; Selection and Training Project of High-level Talents in the Sixteenth Six Talent Peaks of Jiangsu Province[DZXX-045] ; Open Subject of State Key Laboratory of Process Automation in Mining Metallurgy[BGRIMMKZSKL-2019-10] ; Open Subject of State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences[20190105] ; Science and Technology Project of Jiangsu Province[BK20191339] ; Fundamental Research Funds for the Central Universities[2019XKQYMS64] ; Xuzhou Science and Technology Project[KC19055]
项目资助者	National Natural Science Foundation of China ; Selection and Training Project of High-level Talents in the Sixteenth Six Talent Peaks of Jiangsu Province ; Open Subject of State Key Laboratory of Process Automation in Mining Metallurgy ; Open Subject of State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences ; Science and Technology Project of Jiangsu Province ; Fundamental Research Funds for the Central Universities ; Xuzhou Science and Technology Project
WOS研究方向	Automation & Control Systems ; Engineering ; Mathematics
WOS类目	Automation & Control Systems ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic ; Mathematics, Interdisciplinary Applications
WOS记录号	WOS:000604437800030
出版者	PERGAMON-ELSEVIER SCIENCE LTD
七大方向——子方向分类	智能控制
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42542
专题	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
通讯作者	Chu, Fei
作者单位	1.China Univ Min & Technol, Sch Informat & Control Engn, Minist Educ, Underground Space Intelligent Control Engn Res Ct, Xuzhou 221116, Jiangsu, Peoples R China 2.Beijing Gen Res Inst Min & Met, Beijing Key Lab Proc Automat Min & Met, State Key Lab Proc Automat Min & Met, Beijing 100160, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China 5.Univ Surrey, Dept Chem & Proc Engn, Guildford, Surrey, England
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Chu, Fei,Cheng, Xiang,Peng, Chuang,et al. A process transfer model-based optimal compensation control strategy for batch process using just-in-time learning and trust region method[J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS,2021,358(1):606-632.
APA	Chu, Fei,Cheng, Xiang,Peng, Chuang,Jia, Runda,Chen, Tao,&Wei, Qinglai.(2021).A process transfer model-based optimal compensation control strategy for batch process using just-in-time learning and trust region method.JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS,358(1),606-632.
MLA	Chu, Fei,et al."A process transfer model-based optimal compensation control strategy for batch process using just-in-time learning and trust region method".JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 358.1(2021):606-632.