自适应迭代学习控制的发展现状与展望  

On adaptive iterative learning control:the state of the art and perspective

作　　者：李雪芳 李晓东 刘万泉 LI Xue-fang;LI Xiao-dong;LIU Wan-quan(School of Intelligent Systems Engineering,Sun Yat-Sen University,Shenzhen Guangdong 518107,China)

机构地区：[1]中山大学智能工程学院,广东深圳518107

出　　处：《控制理论与应用》2024年第9期1523-1538,共16页Control Theory & Applications

基　　金：国家自然科学基金项目(62373385);国家自然科学基金基础科学中心项目(62188101);广东省自然科学基金项目(2022A1515010881,2022A 1515010260)资助.

摘　　要：本文在不同问题框架下阐述了自适应迭代学习控制方法的研究现状,并介绍了该领域未来的一些研究方向.首先,简要概述了自适应迭代学习控制的分析方法与控制器结构;其次,从系统的结构特征和运行特征两个角度,讨论了近年来自适应迭代学习控制领域的研究热点,包括非参数型不确定性、输入不确定性、状态受限、状态不可测、非重复运动等关键问题.针对每一类问题,指出了自适应迭代学习控制器的设计和分析特点;然后,探讨了数据驱动自适应迭代学习控制的设计方法;最后,提出了自适应迭代学习控制领域的一些开放性的、具有挑战性的关键问题,亟待进一步研究和探索.This work reviews the state of the art in the area of adaptive iterative learning control(AILC)targeting at different problems,in which some possible future research directions are also presented.First of all,we present a brief overview on the analysis tool and design frameworks of AILC.Then,the latest developments in AILC field are discussed from both the aspects of system structure characteristics and operation characteristics,including the issues on non-parametric uncertainties,input nonlinearities/uncertainties,constrained systems,unmeasurable states,non-repeatable factors,etc.For each type of these issues,the characteristics on design and analysis of the controller are presented in details.Furthermore,the design principles of data-driven AILC are discussed.Finally,we summarize some open and challenging issues in AILC,which need to be further explored and investigated.

关 键 词：迭代学习控制 自适应迭代学习控制 数据驱动 非线性系统 复合能量函数 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]