Open access
Date
2023Type
- Journal Article
ETH Bibliography
yes
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Abstract
Online learning algorithms for dynamical systems provide finite time guarantees for control in the presence of sequentially revealed cost functions. We pose the classical linear quadratic tracking problem in the framework of online optimization where the time-varying reference state is unknown a priori and is revealed after the applied control input. We show the equivalence of this problem to the control of linear systems subject to adversarial disturbances and propose a novel online gradient descent-based algorithm to achieve efficient tracking in finite time. We provide a dynamic regret upper bound scaling linearly with the path length of the reference trajectory and a numerical example to corroborate the theoretical guarantees. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000646495Publication status
publishedExternal links
Journal / series
IEEE Control Systems LettersVolume
Pages / Article No.
Publisher
IEEESubject
Optimal tracking; Online ControlOrganisational unit
02650 - Institut für Automatik / Automatic Control Laboratory
Funding
180545 - NCCR Automation (phase I) (SNF)
787845 - Optimal control at large (EC)
Related publications and datasets
Is supplemented by: http://hdl.handle.net/20.500.11850/649544
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ETH Bibliography
yes
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