Abstract
Data-driven iterative learning control can achieve high performance for systems performing repeating tasks without the need for modeling. The aim of this paper is to develop a fast data-driven method for iterative learning control that is suitable for massive MIMO systems through the use of efficient unbiased gradient estimates. A stochastic conjugate gradient descent algorithm is developed that uses dedicated experiments to determine the conjugate search direction and optimal step size at each iteration. The approach is illustrated on a multivariable example, and it is shown that the method is superior to both the earlier stochastic gradient descent and deterministic conjugate gradient descent methods.
Original language | English |
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Title of host publication | Proceedings of the 60th IEEE Conference on Decision and Control, CDC 2021 |
Publisher | IEEE |
Pages | 3749-3754 |
ISBN (Electronic) | 978-1-6654-3659-5 |
DOIs | |
Publication status | Published - 2021 |
Event | 60th IEEE Conference on Decision and Control, CDC 2021 - Austin, United States Duration: 13 Dec 2021 → 17 Dec 2021 |
Conference
Conference | 60th IEEE Conference on Decision and Control, CDC 2021 |
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Country/Territory | United States |
City | Austin |
Period | 13/12/21 → 17/12/21 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.