Identification of multi-sinusoidal signals with direct frequency estimation: An adaptive observer approach

Advanced search
Browse by:

Department | Year

UCL Theses | Latest

Deposit your research

Identification of multi-sinusoidal signals with direct frequency estimation: An adaptive observer approach

Pin, Gilberto; Wang, Yang; Chen, Boli; Parisini, Thomas; (2019) Identification of multi-sinusoidal signals with direct frequency estimation: An adaptive observer approach. Automatica , 99 pp. 338-345. 10.1016/j.automatica.2018.10.026. Green open access

[thumbnail of DIRECTFREQADAPTINMODEL_final.pdf]

Preview

Text
DIRECTFREQADAPTINMODEL_final.pdf - Accepted Version
Download (1MB) | Preview

Abstract

This paper addresses the problem of estimating the frequencies, amplitudes and phases of the n sinusoidal components of a possibly biased multi-sinusoidal signal. The proposed adaptive observer allows the direct adaptation of the frequency estimates with a relatively low dynamic order 3n + 1 (3n for an unbiased signal). The stability analysis proves the global exponential convergence of the estimation error and the robustness to additive norm-bounded measurement perturbations.

Type:	Article
Title:	Identification of multi-sinusoidal signals with direct frequency estimation: An adaptive observer approach
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1016/j.automatica.2018.10.026
Publisher version:	https://doi.org/10.1016/j.automatica.2018.10.026
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Sinusoidal signals; Frequency estimation; Adaptive systems; Estimation algorithms.
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI:	https://discovery.ucl.ac.uk/id/eprint/10189791