批次过程控制——回顾与展望

卢静宜; 曹志兴; 高福荣

doi:10.16383/j.aas.2017.c170131

批次过程控制——回顾与展望

doi: 10.16383/j.aas.2017.c170131

卢静宜^1,,
曹志兴^1,,
高福荣^1,2, ,

1.
香港科技大学霍英东研究院香港中国
2.
香港科技大学化学与生物分子工程学系香港中国

基金项目:

香港研究资助局项目 16233316

国家自然科学基金 61433005

详细信息

作者简介:
卢静宜香港科技大学霍英东研究院博士后, 2011年获得浙江大学自动化专业学士学位, 2016年获得香港科技大学化学与生物分子工程学系博士学位.主要研究方向为模型预测控制, 迭代学系控制及系统辨识.E-mail:jluab@connect.ust.hk

曹志兴爱丁堡大学生物科学学院博士后, 2012年获得浙江大学控制科学与工程学系学士学位, 2016年获得香港科技大学化学工程与生物分子工程学系博士学位.他曾访问斯图加特大学和哈佛大学.主要研究方向为迭代学习控制, 系统辨识, 实时优化及随机过程和控制理论在系统生物学中的应用.E-mail:zcaoab@connect.ust.hk

通讯作者:
高福荣香港科技大学化学与生物分子工程学系讲座教授.1985年获得中国石油大学自动化专业学士学位, 1989年和1993年在加拿大麦吉尔大学获得硕士和博士学位.在执教香港科技大学前, 任职于澳大利亚墨尔本的Moldflow公司.主要研究方向为过程检测与故障诊断, 批次过程控制, 高分子材料加工及优化.E-mail:kefgao@ust.hk

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出版历程
- 收稿日期: 2017-03-10
- 录用日期: 2017-05-06
- 刊出日期: 2017-06-20

Batch Process Control——Overview and Outlook

LU Jing-Yi^1
,,
CAO Zhi-Xing^1
,,
GAO Fu-Rong^{1,2
, ,}

1.
Fok Ying Tung Graduate School, Hong Kong University of Science and Technology, Hong Kong, China
2.
Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Hong Kong, China

Funds:

Research Grants Council 16233316

National Natural Science Foundation of China 61433005

More Information

Author Bio:
Postdoctor at Fok Ying Tung Graduate School, Hong Kong University of Science and Technology. She received her bachelor degree from Zhejiang University in automation in 2011, and Ph.D. degree from Hong Kong University of Science and Technology in chemical engineering in 2016. Her research interest covers model predictive control, iterative learning control, system identification

Postdoctor at the School of Biological Sciences, University of Edinburgh. He received his bachelor degree from the Department of Control Science and Engineering, Zhejiang University, China, in 2012 and his Ph.D. degree in chemical and biomolecular engineering from Hong Kong University of Science and Technology (HKUST) in 2016. He visited the University of Stuttgart and Harvard University. His research interest covers iterative learning control, system identification, real-time optimization, and the application of stochastic process theory & control theory in system biology

Corresponding author: GAO Fu-Rong Chair professor in the Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology (HKUST). He obtained his bachelor degree in automation from East China Institute of Petroleum in 1985, and his master and Ph.D. degrees in chemical engineering from McGill University, Montreal, Canada, in 1989 and 1993, respectively. He worked as a senior research engineer at Moldflow International, Melbourne, Australia, from 1993 to 1995 before joining HKUST as a professor. His research interest covers process monitoring and fault diagnosis, batch process control, polymer processing control, and optimization. Corresponding author of this paper

摘要

摘要: 批次过程是一类重要的化工过程.因其本身的灵活性及高效性，被广泛应用于半导体制造、塑料加工、生物制药等领域.针对批次过程控制算法的研究也得到了大批学者的关注.在近三十年中，批次过程控制理论得到了长足的发展.但由于过程本身复杂的动态特性，以及对控制精度要求的提高，现有的理论和方法仍面临着挑战.本文从批次过程的特性出发，分析了算法设计的难点，对几种重要的控制算法进行总结分析，同时讨论了未来可能的发展方向.
- 批次过程 /
- 过程控制 /
- 迭代学习控制 /
- 二维时间控制
Abstract: Batch process is an important class of chemical processes. Due to its high flexibility and versatility, it has found wide application in industry such as semi-conductor manufacturing, polymer processing, and pharmaceutical production. Meanwhile, efforts have been made in the research on control algorithms for batch processes. In the past 30 years, batch process control has achieved significant developments. However, due to its complexity and increasing requirements on control precision, there are still many challenging problems in this field. In this paper, starting from the nature of batch process, we address the challenges in this field, review the development of control strategies, analyze several classical control algorithms, and discuss the future development of batch process control.
- Batch process /
- process control /
- iterative learning control (ILC) /
- two-time-dimensional (2D) control

HTML全文

图 1 批次过程多重时变特性示意图

Fig. 1 Illustration of the characteristics of batch processes

下载: 全尺寸图片幻灯片

图 2 自适应控制器示意图

Fig. 2 Control scheme of adaptive control

下载: 全尺寸图片幻灯片

图 3 反馈迭代学习控制示意图

Fig. 3 Control scheme of feedback iterative learning control

下载: 全尺寸图片幻灯片

图 4 带终端约束的迭代学习预测控制算法示意图

Fig. 4 Iterative learning predictive control with terminal constraints

下载: 全尺寸图片幻灯片

图 5 不等长现象示意图

Fig. 5 Illustration of uneven length phenomena

下载: 全尺寸图片幻灯片

表 1 批次过程控制技术应用

Table 1 Application of process control

半导体加工	Honeywell^[78]
	Canon^[79]
	Western digital^[80]
间歇反应器	Honeywell^[81]
	Petronetics^[82]
	ABB^[83]
塑料加工	ITT manufacturing^[84]
	Honeywell^[85]
	Nokia^[86]
血糖控制	Animas^[87]
血糖控制	Dexcom^[88]

下载: 导出CSV

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