Transfer learning for batch process optimal control using LV-PTM and adaptive control strategy

doi:10.1016/j.jprocont.2019.06.010

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

	Transfer learning for batch process optimal control using LV-PTM and adaptive control strategy
	Chu, Fei 1,2; Zhao, Xu 1; Yao, Yuan 3; Chen, Tao 4; Wang, Fuli 5
发表期刊	JOURNAL OF PROCESS CONTROL
ISSN	0959-1524
	2019-09-01
卷号	81 页码:197-208
通讯作者	Chu, Fei(chufeizhufei@sina.com)
摘要	In this study, we investigate a data-driven optimal control for a new batch process. Existing data-driven optimal control methods often ignore an important problem, namely, because of the short operation time of the new batch process, the modeling data in the initial stage can be insufficient. To address this issue, we introduce the idea of transfer learning, i.e., a latent variable process transfer model (LV-PTM) is adopted to transfer sufficient data and process information from similar processes to a new one to assist its modeling and quality optimization control. However, due to fluctuations in raw materials, equipment, etc., differences between similar batch process are always inevitable, which lead to the serious and complicated mismatch of the necessary condition of optimality (NCO) between the new batch process and the LV-PTM-based optimization problem. In this work, we propose an LV-PTM-based batch-to-batch adaptive optimal control strategy, which consists of three stages, to ensure the best optimization performance during the whole operation lifetime of the new batch process. This adaptive control strategy includes model updating, data removal, and modifier-adaptation methodology using final quality measurements in response. Finally, the feasibility of the proposed method is demonstrated by simulations. (C) 2019 Published by Elsevier Ltd.
关键词	Transfer learning Data-driven LV-PTM Optimal control Adaptive control strategy
DOI	10.1016/j.jprocont.2019.06.010
关键词[WOS]	PARTICLE-SIZE DISTRIBUTION ; PRODUCT TRANSFER ; OPTIMIZATION ; MODEL ; IDENTIFICATION ; QUALITY
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61503384] ; National Natural Science Foundation of China[61873049] ; Open Fund of National Engineering Research Center of Coal Preparation and Purification[2018NERCCPP-B03] ; 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 Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences[20190105] ; Frontier Scientific Research Projects of China University of Mining and Technology[2019XKQYMS64] ; Postgraduate Research & Practice Innovation Program of Jiangsu Province[SJCX18-0662] ; National Natural Science Foundation of China[61503384] ; National Natural Science Foundation of China[61873049] ; Open Fund of National Engineering Research Center of Coal Preparation and Purification[2018NERCCPP-B03] ; 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 Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences[20190105] ; Frontier Scientific Research Projects of China University of Mining and Technology[2019XKQYMS64] ; Postgraduate Research & Practice Innovation Program of Jiangsu Province[SJCX18-0662]
项目资助者	National Natural Science Foundation of China ; Open Fund of National Engineering Research Center of Coal Preparation and Purification ; Selection and Training Project of High-level Talents in the Sixteenth Six Talent Peaks of Jiangsu Province ; Open Subject of State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences ; Frontier Scientific Research Projects of China University of Mining and Technology ; Postgraduate Research & Practice Innovation Program of Jiangsu Province
WOS研究方向	Automation & Control Systems ; Engineering
WOS类目	Automation & Control Systems ; Engineering, Chemical
WOS记录号	WOS:000486096900017
出版者	ELSEVIER SCI LTD
七大方向——子方向分类	智能控制
引用统计	被引频次：17[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/173211/27239
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, Natl Engn Res Ctr Coal Preparat & Purificat, Xuzhou 221116, Jiangsu, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Natl Tsing Hua Univ, Dept Chem Engn, Hsinchu 30013, Taiwan 4.Univ Surrey, Dept Chem & Proc Engn, Guildford GU2 7XH, GU, England 5.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China
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,Zhao, Xu,Yao, Yuan,et al. Transfer learning for batch process optimal control using LV-PTM and adaptive control strategy[J]. JOURNAL OF PROCESS CONTROL,2019,81:197-208.
APA	Chu, Fei,Zhao, Xu,Yao, Yuan,Chen, Tao,&Wang, Fuli.(2019).Transfer learning for batch process optimal control using LV-PTM and adaptive control strategy.JOURNAL OF PROCESS CONTROL,81,197-208.
MLA	Chu, Fei,et al."Transfer learning for batch process optimal control using LV-PTM and adaptive control strategy".JOURNAL OF PROCESS CONTROL 81(2019):197-208.