CASIA OpenIR
Transfer learning for batch process optimal control using LV-PTM and adaptive control strategy
Chu, Fei1,2; Zhao, Xu1; Yao, Yuan3; Chen, Tao4; Wang, Fuli5
Source PublicationJOURNAL OF PROCESS CONTROL
ISSN0959-1524
2019-09-01
Volume81Pages:197-208
Corresponding AuthorChu, Fei(chufeizhufei@sina.com)
AbstractIn 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.
KeywordTransfer learning Data-driven LV-PTM Optimal control Adaptive control strategy
DOI10.1016/j.jprocont.2019.06.010
WOS KeywordPARTICLE-SIZE DISTRIBUTION ; PRODUCT TRANSFER ; OPTIMIZATION ; MODEL ; IDENTIFICATION ; QUALITY
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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 Research AreaAutomation & Control Systems ; Engineering
WOS SubjectAutomation & Control Systems ; Engineering, Chemical
WOS IDWOS:000486096900017
PublisherELSEVIER SCI LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27239
Collection中国科学院自动化研究所
Corresponding AuthorChu, Fei
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
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.
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