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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

池荣虎 侯忠生 黄彪

池荣虎, 侯忠生, 黄彪. 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. 自动化学报, 2017, 43(6): 917-932. doi: 10.16383/j.aas.2017.c170086
引用本文: 池荣虎, 侯忠生, 黄彪. 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. 自动化学报, 2017, 43(6): 917-932. doi: 10.16383/j.aas.2017.c170086
CHI Rong-Hu, HOU Zhong-Sheng, HUANG Biao. Optimal Iterative Learning Control of Batch Processes: From Model-based to Data-driven. ACTA AUTOMATICA SINICA, 2017, 43(6): 917-932. doi: 10.16383/j.aas.2017.c170086
Citation: CHI Rong-Hu, HOU Zhong-Sheng, HUANG Biao. Optimal Iterative Learning Control of Batch Processes: From Model-based to Data-driven. ACTA AUTOMATICA SINICA, 2017, 43(6): 917-932. 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
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

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

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: 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.
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  • 收稿日期:  2017-02-23
  • 录用日期:  2017-05-11
  • 刊出日期:  2017-06-20

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