Detection of nonlinear distortions with multisine excitations in the case of nonideal behavior of the input signal

A method for the detection, qualification, and quantification of nonlinear distortions on a frequency response function (FRF) measurement was described in previous papers by Schoukens et al.. The kernel idea of that method is to use well-chosen periodic excitations where only some of the considered frequency components are excited. The nonexcited frequency lines (detection lines) are used to detect, qualify, and quantify the nonlinear distortions. Due to the presence of a low-quality generator, a nonlinear actuator, or due to the interaction of the generator with the nonlinear system, unwanted excitation power can be present at the detection lines at the input of the system. In this case, the basic conditions necessary to apply the detection method are no longer valid. In this paper, a first-order compensation method is described that allows the detection, qualification, and quantification of nonlinear distortions on the FRF measurement with the original technique of previous papers by Schoukens et al., in the case that the desired input cannot be applied at the device under test. This first-order compensation method is illustrated on simulations and a real measurement example.     

Frequency domain maximum likelihood estimation of linear dynamic errors-in-variables models

This paper studies the linear dynamic errors-in-variables problem for filtered white noise excitations. First, a frequency domain Gaussian maximum likelihood (ML) estimator is constructed that can handle discrete-time as well as continuous-time models on (a) part(s) of the unit circle or imaginary axis. Next, the ML estimates are calculated via a computationally simple and numerically stable Gauss-Newton minimization scheme. Finally, the Cramér-Rao lower bound is derived.     

Estimation and Validation of Semiparametric Dynamic Nonlinear Models

An approach for measurement-based modeling of nonlinear devices is proposed. The method that is commonly used for linear time-invariant systems, namely, parametric modeling and nonparametric verification, is hereby extended to a class of nonlinear systems. The applicability of the method is illustrated on the baseband modeling of a radio-frequency amplifier over a wide power and frequency range.     

Minimum variance bounds for overparameterized models

In this paper it is shown that the generalized Cramer-Rao lower bound on the estimate of invariants of (over)parameterized models is independent of the particular (over)parameterization chosen and equals that of the identifiable form     

Measurement of noise (cross-) power spectra for frequency-domainsystem identification purposes: large-sample results

Several frequency-domain estimators of parametric transfer function models assume that the (cross-) power spectra of the disturbing noise sources are known. This paper presents a time-efficient method to measure these noise (cross-) power spectra and studies its influence on the asymptotic properties of the estimated model parameters     

Parameter estimation in strongly nonlinear circuits

The combination of the generalized Volterra approach to compute the steady-state output of strongly nonlinear systems with the maximum likelihood estimator (MLE) developed by J. Schoukens et al. (see ibid., vol.IM-37, p.10-17, 1988) yields a powerful tool to estimate the parameters in strongly nonlinear circuits. As a result, it is possible to determine system characteristics that cannot be measured directly or are difficult to obtain. The latter is illustrated by means of an inverting amplifier built around an operational amplifier causing slew-induced distortion. Two different models are used to represent the operational amplifier. The first considers only one nonlinearity, namely a saturating current source characterized by two parameters, but can only describe symmetric slew-induced distortion. The other model uses two diodes and results in a six-parameter model capable of addressing the asymmetric case. By comparing the results obtained for these approaches with measurements on the actual circuit, the capability of the identification technique for strongly nonlinear systems is demonstrated     

Order estimation for linear time-invariant systems using frequencydomain identification methods

A two-step coarse-fine order estimation technique is proposed to determine the order of the numerator and the denominator polynomials of rational transfer function models for single-input/single-output (SISO) linear time-invariant systems. The coarse order estimation is based on rank detection by verification of the stochastic significance of the singular values of a linearized problem. The fine order estimation is based on a statistical analysis of the maximum likelihood cost function. The method is tested on measurements of low-(4 zeros, 6 poles) and high- (58 poles, 58 zeros) order systems     

Generation of enhanced initial estimates for Hammerstein Systems

Hammerstein systems are nonlinear models that are used in many domains for their simplicity and physical meaning. This paper presents a simple iterative method for generating good starting values which can be used to initialize the numerical nonlinear optimization of the cost function. In the absence of model errors, the presented method is proven to converge locally to the true model values in the noise-less case.     

Estimation of the risk for an unstable behaviour of feedback systems in the presence of nonlinear distortions

A criterion to verify experimentally the stability of a nonlinear system, captured in a feedback loop, is proposed. The basic idea is to split the output power in coherent (linearly related to the input) and noncoherent power (the remaining power). A nonlinear power gain, measuring the sensitivity of the noncoherent output power to input variations, is introduced. Using the small gain theorem, it is possible to check the local stability of the feedback for the actual class of excitation signals: the risk is estimated that the feedback system would become unstable for another realization of the experiment using extreme value statistics.     

Modifications of paper and paperboard surfaces with a nanostructured polymer coating

Organic nanoparticles synthesized by imidization of styrene-maleic anhydride copolymers are deposited as a top-coating onto paper and paperboard substrates from a stable aqueous dispersion with maximum solid content of 35 wt.%. The morphology, physical characteristics and chemical surface properties of the coatings are discussed in this paper, using scanning electron microscopy, atomic force microscopy, contact angle measurements and Raman spectroscopy. Due to the high glass transition temperature of the polymer nanoparticles, a unique micro- to nanoscale structured coating is formed that favourably improves the gloss, printing properties (ink-jet printing test and off-set printing test), surface hydrophobicity (maximum water contact angle 140°) and water repellence (reduction of Cobb-values). The interaction of the nanoparticle coatings with the cellulosic paper web results in improvement of the mechanical paper strength and is attributed to hydrogen-bonding between the nanoparticles and the cellulosic fibers.