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Proceedings ArticleDOI

Bias Removal in Higher Order Sinusoidal Input Describing Functions

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TLDR
A novel method is presented for the reduction of bias caused by harmonic excitation in the identification of higher order sinusoidal input describing functions (HOSIDF) and is demonstrated with real measurements on a mechanical system with friction.
Abstract
In this paper a novel method is presented for the reduction of bias caused by harmonic excitation in the identification of higher order sinusoidal input describing functions (HOSIDF). HOSIDF are a recently introduced generalization of the theory of the describing function. HOSIDF describe the magnitude and phase relations between the individual harmonic components in the output signal of a non-linear system and the sinusoidal excitation signal. In the presented method, the output signal of a non-linear system subjected to harmonic excitation is numerically split up into a fraction caused by the non-linear response due to the fundamental input signal component and the fraction caused by the quasi-linear response due to the harmonic input signal components. This separation is based on the assumption that the non-linear effects of intermodulation can be neglected, compared to the the effects caused by the generation of harmonics and gain compression/expansion. The method is demonstrated with real measurements on a mechanical system with friction.

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Citations
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Identification of Linear Systems with Nonlinear Distortions

TL;DR: A theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'.
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Frequency domain‐based nonlinearity detection and compensation in Lur'e systems

TL;DR: In this article, the authors proposed a frequency domain-based method for detection and optimal compensation of performance degrading nonlinear effects in Lur'e-type systems, where a sinusoidal response is necessary and sufficient to show the existence of an equivalent linear and time invariant dynamical model that fully captures the system dynamics for a well defined set of input signals and initial conditions.
References
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Measuring Volterra kernels

TL;DR: In this paper, the authors introduce a pseudonoise test for measuring the second Volterra kernel of a weakly nonlinear system, which is analogous to the one we present in this paper.
Journal ArticleDOI

Spectral analysis for non-linear systems, Part I: Parametric non-linear spectral analysis

TL;DR: A new theory of spectral analysis for non-linear systems is introduced which consists of estimating the parameters in a NARMAX model description of the system and then computing the generalised frequency response functions directly from the estimated model.
Journal ArticleDOI

Identification of linear systems with nonlinear distortions

TL;DR: In this article, the impact of nonlinear distortions on linear system identification was studied and a theoretical framework was proposed that extends the linear system description to include nonlinear distortion: the nonlinear system is replaced by a linear model plus a nonlinear noise source.
Journal ArticleDOI

Higher-order sinusoidal input describing functions for the analysis of non-linear systems with harmonic responses

TL;DR: In this paper, an extension to higher-order describing functions is realized by introducing the concept of the harmonics generator, which relates the magnitude and phase of the higher harmonics of the periodic response of the system to the magnitude of a sinusoidal excitation.
Journal ArticleDOI

Spectral analysis for non-linear systems, Part II: Interpretation of non-linear frequency response functions

TL;DR: In this paper, the interpretation and properties of the non-linear frequency response functions are discussed and illustrated by example, and new methods of parametric spectral analysis for a wide class of nonlinear systems were introduced.
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