Open access
Autor(in)
Alle anzeigen
Datum
2023Typ
- Journal Article
ETH Bibliographie
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. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000646495Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
IEEE Control Systems LettersBand
Seiten / Artikelnummer
Verlag
IEEEThema
Optimal tracking; Online ControlOrganisationseinheit
02650 - Institut für Automatik / Automatic Control Laboratory
Förderung
180545 - NCCR Automation (phase I) (SNF)
787845 - Optimal control at large (EC)
Zugehörige Publikationen und Daten
Is supplemented by: http://hdl.handle.net/20.500.11850/649544
ETH Bibliographie
yes
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