Iterative Learning Control With Incomplete Information: A Survey

Dong Shen

doi:10.1109/JAS.2018.7511123

Volume 5 Issue 5

Aug. 2018

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2018 > 5(5): 885-901

Dong Shen, "Iterative Learning Control With Incomplete Information: A Survey," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 885-901, Sept. 2018. doi: 10.1109/JAS.2018.7511123

Citation:

Dong Shen, "Iterative Learning Control With Incomplete Information: A Survey," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 885-901, Sept. 2018. doi: 10.1109/JAS.2018.7511123

Dong Shen, "Iterative Learning Control With Incomplete Information: A Survey," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 885-901, Sept. 2018. doi: 10.1109/JAS.2018.7511123

Citation:

Dong Shen, "Iterative Learning Control With Incomplete Information: A Survey," IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 885-901, Sept. 2018. doi: 10.1109/JAS.2018.7511123

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Iterative Learning Control With Incomplete Information: A Survey

doi: 10.1109/JAS.2018.7511123

Dong Shen^,

Beijing University of Chemical Technology, Beijing 100029, China

Funds:

the National Natural Science Foundation of China 61673045

Beijing Natural Science Foundation 4152040

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Abstract

Abstract

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.
- Data dropout,
- data robustness,
- incomplete information,
- iterative learning control (ILC),
- quantized control,
- sampled control,
- varying lengths

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References(102)

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Iterative Learning Control With Incomplete Information: A Survey

doi: 10.1109/JAS.2018.7511123

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