Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems

Shenyu Liu; Kaiwen Chen; Jaap Eising

doi:10.1109/CDC49753.2023.10383840

Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems

Shenyu Liu, Kaiwen Chen, Jaap Eising

自动化学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

This paper proposes a novel online data-driven adaptive control for discrete-time unknown linear time-varying systems. Initialized with an empirical feedback gain, the algorithm periodically updates this gain based on the data collected over a short time window before each update. Meanwhile, the stability of the closed-loop system is analyzed in detail, which shows that under some mild assumptions, the proposed online data-driven adaptive control scheme can guarantee practical global exponential stability. Finally, the proposed algorithm is demonstrated by numerical simulations and its performance is compared with other control algorithms for unknown linear time-varying systems.

源语言	英语
主期刊名	2023 62nd IEEE Conference on Decision and Control, CDC 2023
出版商	Institute of Electrical and Electronics Engineers Inc.
页	8775-8780
页数	6
ISBN（电子版）	9798350301243
DOI	https://doi.org/10.1109/CDC49753.2023.10383840
出版状态	已出版 - 2023
活动	62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, 新加坡期限: 13 12月 2023 → 15 12月 2023

出版系列

姓名	Proceedings of the IEEE Conference on Decision and Control
ISSN（印刷版）	0743-1546
ISSN（电子版）	2576-2370

会议

会议	62nd IEEE Conference on Decision and Control, CDC 2023
国家/地区	新加坡
市	Singapore
时期	13/12/23 → 15/12/23

访问文件

10.1109/CDC49753.2023.10383840

其它文件与链接

链接到 Scopus 的出版物

引用此

@inproceedings{a1c8e10956744eafa0aa880c83f0043b,

title = "Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems",

abstract = "This paper proposes a novel online data-driven adaptive control for discrete-time unknown linear time-varying systems. Initialized with an empirical feedback gain, the algorithm periodically updates this gain based on the data collected over a short time window before each update. Meanwhile, the stability of the closed-loop system is analyzed in detail, which shows that under some mild assumptions, the proposed online data-driven adaptive control scheme can guarantee practical global exponential stability. Finally, the proposed algorithm is demonstrated by numerical simulations and its performance is compared with other control algorithms for unknown linear time-varying systems.",

author = "Shenyu Liu and Kaiwen Chen and Jaap Eising",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 62nd IEEE Conference on Decision and Control, CDC 2023 ; Conference date: 13-12-2023 Through 15-12-2023",

year = "2023",

doi = "10.1109/CDC49753.2023.10383840",

language = "English",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "8775--8780",

booktitle = "2023 62nd IEEE Conference on Decision and Control, CDC 2023",

address = "United States",

}

Liu, S, Chen, K & Eising, J 2023, Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems. 在 2023 62nd IEEE Conference on Decision and Control, CDC 2023. Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., 页码 8775-8780, 62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, 新加坡, 13/12/23. https://doi.org/10.1109/CDC49753.2023.10383840

Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems. / Liu, Shenyu; Chen, Kaiwen; Eising, Jaap.
2023 62nd IEEE Conference on Decision and Control, CDC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. 页码 8775-8780 (Proceedings of the IEEE Conference on Decision and Control).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems

AU - Liu, Shenyu

AU - Chen, Kaiwen

AU - Eising, Jaap

PY - 2023

Y1 - 2023

N2 - This paper proposes a novel online data-driven adaptive control for discrete-time unknown linear time-varying systems. Initialized with an empirical feedback gain, the algorithm periodically updates this gain based on the data collected over a short time window before each update. Meanwhile, the stability of the closed-loop system is analyzed in detail, which shows that under some mild assumptions, the proposed online data-driven adaptive control scheme can guarantee practical global exponential stability. Finally, the proposed algorithm is demonstrated by numerical simulations and its performance is compared with other control algorithms for unknown linear time-varying systems.

AB - This paper proposes a novel online data-driven adaptive control for discrete-time unknown linear time-varying systems. Initialized with an empirical feedback gain, the algorithm periodically updates this gain based on the data collected over a short time window before each update. Meanwhile, the stability of the closed-loop system is analyzed in detail, which shows that under some mild assumptions, the proposed online data-driven adaptive control scheme can guarantee practical global exponential stability. Finally, the proposed algorithm is demonstrated by numerical simulations and its performance is compared with other control algorithms for unknown linear time-varying systems.

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U2 - 10.1109/CDC49753.2023.10383840

DO - 10.1109/CDC49753.2023.10383840

M3 - Conference contribution

AN - SCOPUS:85184804879

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 8775

EP - 8780

BT - 2023 62nd IEEE Conference on Decision and Control, CDC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 62nd IEEE Conference on Decision and Control, CDC 2023

Y2 - 13 December 2023 through 15 December 2023

ER -

Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems

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