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An adaptive-observer-based robust estimator of multi-sinusoidal signals

Chen, B; Pin, G; Ng, WM; Hui, SYR; Parisini, T; (2018) An adaptive-observer-based robust estimator of multi-sinusoidal signals. IEEE Transactions on Automatic Control , 63 (6) pp. 1528-1541. 10.1109/TAC.2017.2752007. Green open access

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Abstract

This paper presents an adaptive-observer-based robust estimation methodology of the amplitudes, frequencies, and phases of biased multi-sinusoidal signals in the presence of bounded perturbations on the measurement. The parameters of the sinusoidal components are estimated online, and the update laws are individually controlled by an excitation-based switching logic enabling the update of a parameter only when the measured signal is sufficiently informative. This way doing, the algorithm is able to tackle the problem of overparameterization (i.e., when the internal model accounts for a number of sinusoids that is larger than the true spectral content) or temporarily fading sinusoidal components. The stability analysis proves the existence of a tuning parameter set, for which the estimator's dynamics are input-to-state stable with respect to bounded measurement disturbances. The performance of the proposed estimation approach is evaluated and compared with the other existing tools by extensive simulation trials and real-time experiments.

Type: Article
Title: An adaptive-observer-based robust estimator of multi-sinusoidal signals
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TAC.2017.2752007
Publisher version: http://dx.doi.org/10.1109/TAC.2017.2752007
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.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10107501
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