间歇过程最优迭代学习控制的发展：从基于模型到数据驱动

池荣虎; 侯忠生; 黄彪

doi:10.16383/j.aas.2017.c170086

间歇过程最优迭代学习控制的发展：从基于模型到数据驱动

doi: 10.16383/j.aas.2017.c170086

池荣虎^1, ,,
侯忠生^2,,
黄彪^3,

1.
青岛科技大学自动化与电子工程学院青岛 266042 中国
2.
北京交通大学先进控制系统研究所北京 100044 中国
3.
阿尔伯塔大学化学与材料工程学院埃德蒙顿 T6G2G6 加拿大

基金项目:

国家自然科学基金 61433002

国家自然科学基金 61374102

详细信息

作者简介:
侯忠生北京交通大学先进控制系统研究所教授.1994年获得东北大学博士学位.主要研究方向为无模型自适应控制, 数据驱动控制, 学习控制, 智能交通系统.E-mail:zhshhou@bjtu.edu.cn

黄彪加拿大阿尔伯塔大学化学与材料工程学院教授.1997年获得阿尔伯塔大学博士学位.主要研究方向为过程控制, 系统辨识, 控制性能评价, 贝叶斯方法和状态估计.E-mail:bhuang@ualberta.ca

通讯作者:
池荣虎青岛科技大学自动化与电子工程学院教授.2007年获得北京交通大学博士学位.主要研究方向为数据驱动控制, 学习控制, 智能交通系统.本文通信作者.E-mail:ronghuchi@hotmail.com

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出版历程
- 收稿日期: 2017-02-23
- 录用日期: 2017-05-11
- 刊出日期: 2017-06-20

Optimal Iterative Learning Control of Batch Processes: From Model-based to Data-driven

1.
School of Automation & Electronics Engineering, Qingdao University of Science & Technology, Qingdao 266042, China
2.
Advanced Control Systems Laboratory, School of Electronics & Information Engineering, Beijing Jiaotong University, Beijing 100044, China
3.
Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G2G6, Canada

Funds:

National Natural Science Foundation of China 61433002

National Natural Science Foundation of China 61374102

More Information

Author Bio:
Professor at the Advanced Control Systems Laboratory, Beijing Jiaotong University. He received his Ph.D. degree from Northeastern University in 1994. His research interest covers model-free adaptive control, data-driven control, learning control, intelligent traffic systems

Professor in the Department of Chemical and Materials, University of Alberta. He received his Ph.D. degree from University of Alberta, Canada in 1997. His research interest covers process control, system identification, control performance assessment, Bayesian methods, and state estimation.

Corresponding author: CHI Rong-Hu Professor at the School of Automation & Electronics Engineering, Qingdao University of Science & Technology. He received his Ph.D. degree from Beijing Jiaotong University in 2007. His research interest covers data-driven control, learning control, intelligent traffic systems. Corresponding author of this paper

摘要

摘要: 本文综述了间歇过程的基于模型的和数据驱动的最优迭代学习控制方法.基于模型的最优迭代学习控制方法需要已知被控对象精确的线性模型，其研究较为成熟和完善，有着系统的设计方法和分析工具.数据驱动的最优迭代学习控制系统设计和分析的关键是非线性重复系统的迭代动态线性化.本文简要综述了基于模型的最优迭代学习控制的研究进展，详细回顾了数据驱动的迭代动态线性化方法，包括其详细的推导过程和突出的特点.回顾和讨论了广义的数据驱动最优迭代学习控制方法，包括完整轨迹跟踪的数据驱动最优迭代学习控制方法，提出和讨论了多中间点跟踪的数据驱动最优点到点迭代学习控制方法，和终端输出跟踪的数据驱动最优终端迭代学习控制方法.进一步，迭代学习控制研究中的关键问题，如随机迭代变化初始条件、迭代变化参考轨迹、输入输出约束、高阶学习控制律、计算复杂性等.本文突出强调了基于模型的和数据驱动的最优迭代学习控制方法各自的特点与区别联系，以方便读者理解.最后，本文提出数据驱动的迭代学习控制方法已成为越来越复杂间歇过程控制发展的未来方向，一些开放的具有挑战性的问题还有待于进一步研究.
- 间歇过程 /
- 基于模型的最优迭代学习控制 /
- 迭代动态线性化 /
- 数据驱动的最优迭代学习控制
Abstract: A brief overview on model-based optimal iterative learning control (ILC) and data-driven optimal ILC for batch processes is presented. Model-based optimal ILC relies on an exactly known linear model. There are many systematic methods and tools for the optimal ILC controller design and analysis. The foundational of design and analysis tool of data-driven optimal ILC methods for nonlinear repetitive processes is iterative dynamic linearization. This work briefly reviews the model-based optimal ILC with its latest development. The data-driven iterative dynamic linearization method is revisited in detail with its properties and distinct features. The general data-driven optimal iterative learning control, including data-driven optimal ILC for a complete trajectory tracking, data-driven optimal point-to-point ILC for multiple intermediate points tracking, and data-driven optimal terminal ILC for the terminal output tracking, is overviewed and discussed. The key issues in terms of research of optimal ILC, such as stochastic initial conditions, iteration-varying reference trajectory/points, input and output constraints, high-order learning laws, and computational complexity are also presented and discussed. Moreover, this paper highlights and compares the model-based optimal ILC and the generalized data-driven optimal ILC, and demonstrates their relation and difference to facilitate general understanding of these methods. Finally, it is shown that the data-driven ILC methods are receiving increasing interest owing to the increasing complexity of batch processes. Some corresponding challenging problems are presented as well.
- Batch processes /
- model-based optimal iterative learning control /
- iterative dynamic linearization /
- data-driven optimal iterative learning control

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间歇过程最优迭代学习控制的发展：从基于模型到数据驱动

doi: 10.16383/j.aas.2017.c170086

通讯作者:
池荣虎青岛科技大学自动化与电子工程学院教授.2007年获得北京交通大学博士学位.主要研究方向为数据驱动控制, 学习控制, 智能交通系统.本文通信作者.E-mail:ronghuchi@hotmail.com

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Optimal Iterative Learning Control of Batch Processes: From Model-based to Data-driven

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间歇过程最优迭代学习控制的发展：从基于模型到数据驱动

doi: 10.16383/j.aas.2017.c170086

通讯作者: 池荣虎 青岛科技大学自动化与电子工程学院教授.2007年获得北京交通大学博士学位.主要研究方向为数据驱动控制, 学习控制, 智能交通系统.本文通信作者.E-mail:ronghuchi@hotmail.com

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Optimal Iterative Learning Control of Batch Processes: From Model-based to Data-driven

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通讯作者:
池荣虎青岛科技大学自动化与电子工程学院教授.2007年获得北京交通大学博士学位.主要研究方向为数据驱动控制, 学习控制, 智能交通系统.本文通信作者.E-mail:ronghuchi@hotmail.com