Iterative Learning Control With Incomplete Information： A Survey  

作　　者：Dong Shen Senior Member IEEE DongShen

机构地区：[1]ieee [2]beijing university of chemical technology

出　　处：《IEEE/CAA Journal of Automatica Sinica》2018年第5期885-901,共17页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(61673045);Beijing Natural Science Foundation(4152040)

摘　　要：Abstract--This paper conducts a survey on iterative learn- ing control （ILC） with incomplete information and associated control system design, which is a frontier of the ILC field. The incomplete information, including passive and active types, can cause data loss or fragment due to various factors. Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection, storage, transmission, and processing, such as data dropouts, delays, disordering, and limited transmission bandwidth. Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied, such as sampling and quantization. This survey emphasizes two aspects： the first one is how to guarantee good learning performance and tracking performance with passive incomplete data, and the second is how to balance the control performance index and data demand by active means. The promising research directions along this topic are also addressed, where data robustness is highly emphasized. This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance, quantitatively, and promote further developments of ILC theory. Index Terms--Data dropout, data robustness, incomplete in- formation, iterative learning controi（ILC）, quantized control, sampled control, varying lengths.This paper conducts a survey on iterative learning control(ILC) with incomplete information and associated control system design, which is a frontier of the ILC field.The incomplete information, including passive and active types,can cause data loss or fragment due to various factors. Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection,storage, transmission, and processing, such as data dropouts,delays, disordering, and limited transmission bandwidth. Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied, such as sampling and quantization. This survey emphasizes two aspects:the first one is how to guarantee good learning performance and tracking performance with passive incomplete data, and the second is how to balance the control performance index and data demand by active means. The promising research directions along this topic are also addressed, where data robustness is highly emphasized. This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance, quantitatively, and promote further developments of ILC theory.

关 键 词：Data dropout data robustness incomplete information iterative learning control(ILC) quantized control sampled control varying lengths 

分 类 号：TP273.22[自动化与计算机技术—检测技术与自动化装置]