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Auteurs principaux: Liu, Shenyu, Chen, Kaiwen, Eising, Jaap
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:https://arxiv.org/abs/2401.15278
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author Liu, Shenyu
Chen, Kaiwen
Eising, Jaap
author_facet Liu, Shenyu
Chen, Kaiwen
Eising, Jaap
contents This paper proposes a novel online data-driven adaptive control for 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.
format Preprint
id arxiv_https___arxiv_org_abs_2401_15278
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems
Liu, Shenyu
Chen, Kaiwen
Eising, Jaap
Systems and Control
This paper proposes a novel online data-driven adaptive control for 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.
title Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems
topic Systems and Control
url https://arxiv.org/abs/2401.15278