迭代学习模型预测控制研究现状与挑战

马乐乐; 刘向杰; 高福荣

doi:10.16383/j.aas.c210818

迭代学习模型预测控制研究现状与挑战

doi: 10.16383/j.aas.c210818

1.
华北电力大学控制与计算机工程学院北京 102206 中国
2.
香港科技大学化学与生物分子工程学系香港 999077 中国

基金项目: 国家自然科学基金(62073136, U1709211), 中国博士后科学基金(2021M701184), 国家重点研发计划(2021YFE0190900)资助

详细信息

作者简介:
马乐乐：华北电力大学控制与计算机工程学院讲师. 2016年于华北电力大学获自动化专业学士学位. 2021年于华北电力大学获控制理论与控制工程专业博士学位. 主要研究方向为迭代学习模型预测控制理论及其应用. E-mail: malele@ncepu.edu.cn

刘向杰：华北电力大学控制与计算机工程学院教授. 1989年获东北大学自控系工业电气自动化专业学士学位. 1997年获东北大学自动化研究中心博士学位. 主要研究方向为先进控制策略在电力生产过程中的应用. 本文通信作者. E-mail: liuxj@ncepu.edu.cn

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. 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: 2021-08-31
- 录用日期: 2022-01-11
- 网络出版日期: 2022-02-05
- 刊出日期: 2022-06-02

Status and Challenges of Iterative Learning Model Predictive Control

1.
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
2.
Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Hong Kong 999077, China

Funds: Supported by National Natural Science Foundation of China (62073136, U1709211), China Postdoctoral Science Foundation (2021M701184), and National Key Research and Development Program of China (2021YFE0190900)

More Information

Author Bio:
MA Le-Le　Lecturer at the School of Control and Com-puter Engineering, North China Electric Power University. She received her bachelor degree in automation from North China Electric Power University in 2016, and received her Ph.D. degree from North China Electric Power University in 2021. Her research inter-est covers iterative learning model predictive control and its application

LIU Xiang-Jie　Professor at the School of Control and Computer Engineering, North China Electric Power University. He received his bachelor degree from North-eastern University in 1989, and Ph.D. degree from the Research Center of Automation, Northeastern Univer-sity in 1997. His research interest covers application of advanced control strategy in power process control. Corresponding author of this paper

GAO Fu-Rong　Chair professor in the Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology (HKUST), Hong Kong, China. He received 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. His research interest covers process monitoring and fault diagnosis, batch process control, and polymer processing control and optimization

摘要

摘要: 历经20多年的发展, 迭代学习模型预测控制在理论和应用方面都取得了长足的进步. 但由于批次工业过程复杂多样、结构各异、精细化程度较高, 现有的迭代学习模型预测控制理论仍面临着巨大挑战. 本文简要回顾了迭代学习模型预测控制理论的产生及发展, 阐述了二维预测模型、控制律迭代优化及二维稳定性等基本理论问题; 分析了现有方法在理论及应用方面的局限性, 说明了迭代学习模型预测控制在迭代建模、高效优化、变工况适应等方面面临的难点问题, 提出了可行的解决方案. 简要综述了近年来迭代学习模型预测控制理论和应用层面的发展动态, 指出了研究复杂非线性系统、快速系统、变工况系统对进一步完善其理论体系和拓宽其应用前景的意义, 展望了成品质量控制和动态经济控制等重要的未来研究方向.
- 迭代学习模型预测控制 /
- 二维预测模型 /
- 控制律迭代优化 /
- 复杂非线性系统 /
- 快速系统 /
- 变工况
Abstract: After more than 20 years of development, iterative learning model predictive control (ILMPC) has made great progress in the aspects of theory and application. Since batch processes are featured by strong complexity, diversity, structural variety and high degree of refinement, the existing theories are still faced with great challenges. This paper reviews the generation and development of ILMPC, and expounds the basic theory of two-dimensional predictive model, iterative optimization of control law and two-dimensional stability; The limitations of existing methods are analysed from the perspectives of theory and application, and the issues on iterative modelling, efficient optimization and variable operation conditions are discussed in detail with available solutions provided. The new trends in the theory and application of ILMPC are comprehensively reviewed. The studies on complex nonlinear system, fast system and off-design system are clarified to make great significance for further perfecting ILMPC theory and broadening its application. The important future research directions consisting of end product quality control and dynamic economic control are outlined.
- Iterative learning model predictive control (ILMPC) /
- two-dimensional predictive model /
- iterative optimization of control law /
- complex nonlinear system /
- fast system /
- iteration-varying conditions

HTML全文

图 1 批次过程控制发展历程

Fig. 1 Development of batch process control

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图 2 迭代学习模型预测控制结构

Fig. 2 Structure of ILMPC

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图 3 结构及主要内容

Fig. 3 The structure and main contents

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图 4 整体式ILMPC结构

Fig. 4 Scheme of integrated ILMPC

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图 5 Two-stage ILMPC结构

Fig. 5 Scheme of two-stage ILMPC

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图 6 ILMPC的挑战

Fig. 6 Challenges of ILMPC

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图 7 QBMPC结构

Fig. 7 Scheme of QBMPC

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图 8 传统分层控制结构与ILEMPC结构对比

Fig. 8 Comparison of hierarchical control structure and ILEMPC structure

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表 1 迭代学习模型预测控制分类

Table 1 Categories of ILMPC

分类依据	类别	优势	局限性
模型形式	2D输入输出预测模型^{[36−37, 42−46]}	便于直接推导迭代关系	无法表征系统内部动态
	2D状态空间预测模型^[47−54]	便于系统性能定性分析	需完全掌握系统状态信息
	2D非线性隐式预测模型^[55−57]	精确描述非线性动态特性	优化计算复杂度增加
优化结构	整体式结构^{[37, 42−44]}	便于性能分析, 计算负担小	学习作用难以充分发挥
优化结构	两段式结构^{[39−40, 55, 62]}	避免随机过程信息影响	性能分析较难, 计算负担大

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