Adaptive Estimation of Time-Varying Parameters using DREM

Emil Lykke Diget*, Christoffer Sloth

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

In this paper we present a method for estimating time-varying parameters in a linear regression equation. We combine local polynomial regression with dynamic regressor extension and mixing to independently estimate the parameters. During local polynomial regression, a time-varying parameter is approximated by locally constant polynomial coefficients. We propose to use the Bernstein basis instead of the commonly used monomial basis to improve numerical conditioning. A simulation example shows that our proposed estimator has improved performance compared to a similar method and allows a higher polynomial order.
Original languageEnglish
Title of host publication2023 62nd IEEE Conference on Decision and Control (CDC)
PublisherIEEE
Publication dateDec 2023
Pages3186-3191
ISBN (Electronic)979-8-3503-0124-3
DOIs
Publication statusPublished - Dec 2023
Event2023 62nd IEEE Conference on Decision and Control (CDC) - Singapore, Singapore
Duration: 13. Dec 202315. Dec 2023

Conference

Conference2023 62nd IEEE Conference on Decision and Control (CDC)
Country/TerritorySingapore
CitySingapore
Period13/12/202315/12/2023
SeriesIEEE Conference on Decision and Control. Proceedings
ISSN0743-1546

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