Robust Sinusoid Identification With Structured and Unstructured Measurement Uncertainties

Pin, Gilberto; Chen, Boli; Parisini, Thomas; Bodson, Marc

doi:10.1109/tac.2013.2293415

Cited by 25 publications

(22 citation statements)

References 17 publications

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“…Moreover, the assumed norm-boundd on the output noise implies the existence of d η such thatd η > 0 : |d η (t)| ≤d η , ∀t ∈ R ≥0 . Now, we introduce the structure of the adaptive observer for joint estimation of z(t) and θ * in (15), and in turn estimating the frequenciesΩ i , i = 1, · · · , n. Besides the measured output filter (12), the architecture of the estimator also includes three dynamic components (16), (17) and (19), which are described below: 1) Augmented state estimator:…”

Section: Filtered-augmentation-based Adaptive Observermentioning

confidence: 99%

“…The OSG-SOGI architecture is also exploited in [10] and [11] to cope with the biased signal case, namely, in [11] the OSG-SOGI is extended to the thirdorder generalized integrator-based OSG (OSG-TOGI) that is characterized by an adaptive resonant frequency. Apart from the aforementioned methods, a number of nonlinear estimation algorithms employing suitable pre-filtering techniques also have been presented in literature to address the AFP estimation in presence of an unknown bias (see, for example, [12], [13], [14], [15], [16] and the references cited therein).…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive-Observer-Based Robust Estimator of Multi-sinusoidal Signals

Chen

et al. 2018

IEEE Trans. Automat. Contr.

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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 presence of bounded perturbations on the measurement. The parameters of the sinusoidal components are estimated on-line 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 over-parametrization (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 other existing tools by extensive simulation trials and real-time experiments.

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Section: Filtered-augmentation-based Adaptive Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive-Observer-Based Robust Estimator of Multi-sinusoidal Signals

Chen

et al. 2018

IEEE Trans. Automat. Contr.

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“…In the papers [27,28,23], a set of cascaded first-order low-pass (LP) filters, called "pre-filter" is exploited with the aim of both cancelling the effect of structured "time-polynomial" perturbations (such as bias and linear drift) and obtaining auxiliary signals that can be exploited to estimate the frequency and the amplitude of the sinusoid. These auxiliary signals can be used either directly [27,28] or indirectly [23] to estimate the unknown frequency and the amplitude of the measured noisy sinusoid with high noise immunity.…”

Section: Introductionmentioning

confidence: 99%

“…These auxiliary signals can be used either directly [27,28] or indirectly [23] to estimate the unknown frequency and the amplitude of the measured noisy sinusoid with high noise immunity.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, while in [23,27,28] the pre-filter was conceived as a cascade of first-order LP filters, in the present paper we propose to use a parallel pre-filtering scheme, where two LP filters with different poles are used to generate auxiliary signals from which the needed information is extracted. This enhanced structure allows to simplify the adaptation law with respect to [28,23], while maintaining robustness with respect to bounded measurement perturbations.…”

Section: Introductionmentioning

confidence: 99%

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A parallel prefiltering approach for the identification of a biased sinusoidal signal: Theory and experiments

Chen

et al. 2015

Adaptive Control & Signal

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SUMMARYThe problem of estimating the amplitude, frequency and phase of an unknown sinusoidal signal from a noisy biased measurement is addressed in this paper by a family of parallel pre-filtering schemes. The proposed methodology consists in using a pair of linear filters of specified order to generate a suitable number of auxiliary signals that are used to estimate -in an adaptive way -the frequency, the amplitude and the phase of the sinusoid. Increasing the order the pre-filters improves the noise immunity of the estimator, at the cost of an increase of the computational complexity. Among the whole family of estimators realizable by varying the order of the filters, the simple parallel pre-filters of order 2+2 and 3+3 are discussed in detail, being the most attractive from the implementability point of view. The behavior of the two algorithms with respect to bounded external disturbances is characterized by Input-to-State Stability arguments. Finally, the effectiveness of the proposed technique is shown both by comparative numerical simulations and by a real experiment addressing the estimation of the frequency of the electrical mains from a noisy voltage measurement.

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Adaptive filters cascade applied to a frequency identification improvement problem

Aranovskiy

Bobtsov

Pyrkin

et al. 2015

Adaptive Control & Signal

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Problem of frequency identification performance improvement for a single-tone sinusoidal signal is solved via construction of an adaptive filters cascade. The cascade consists of adaptive band-pass filters tuned by estimates of the frequency provided by a given identification algorithm. Stability of the cascade is studied and boundedness of trajectories is proven with Lyapunov analysis under certain assumption on identification algorithm. Numerical simulations are given illustrating improved identification performance for different identification algorithms.

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Robust Sinusoid Identification With Structured and Unstructured Measurement Uncertainties

Cited by 25 publications

References 17 publications

An Adaptive-Observer-Based Robust Estimator of Multi-sinusoidal Signals

An Adaptive-Observer-Based Robust Estimator of Multi-sinusoidal Signals

A parallel prefiltering approach for the identification of a biased sinusoidal signal: Theory and experiments

Adaptive filters cascade applied to a frequency identification improvement problem

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