自动化学报2017，Vol.43Issue(6)：917-932,16.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
Optimal Iterative Learning Control of Batch Processes: From Model-based to Data-driven
摘要
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.关键词
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Key words
Batch processes/model-based optimal iterative learning control/iterative dynamic linearization/data-driven optimal iterative learning control引用本文复制引用
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[J].自动化学报,2017,43(6):917-932,16.基金项目
国家自然科学基金(61374102,61433002),山东省泰山学者工程资助 Supported by National Natural Science Foundation of China (61374102,61433002),Taishan Scholar Program of Shandong Province of China （61374102,61433002）
