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

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2023 62nd IEEE Conference on Decision and Control, CDC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	8775-8780
Number of pages	6
ISBN (Electronic)	9798350301243
DOIs	https://doi.org/10.1109/CDC49753.2023.10383840
Publication status	Published - 2023
Event	62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore Duration: 13 Dec 2023 → 15 Dec 2023

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	62nd IEEE Conference on Decision and Control, CDC 2023
Country/Territory	Singapore
City	Singapore
Period	13/12/23 → 15/12/23

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10.1109/CDC49753.2023.10383840

Cite this

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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. in 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., pp. 8775-8780, 62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, 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. p. 8775-8780 (Proceedings of the IEEE Conference on Decision and Control).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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

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T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 8775

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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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