Accurate estimation of the non-parametric FRF of lightly-damped mechanical systems using arbitrary excitations

Abstract Lightly-damped mechanical systems exhibit strong resonant behaviour which could potentially result in life-threatening situations. To prevent these situations from happening, frequency response function measurements are essential to accurately characterise the resonant modes of the mechanical system. Unfortunately, these measurements are distorted by leakage and (long) transient phenomena. Local modelling techniques have been introduced in the past to resolve these complications but either they do not use the correct model structure or they introduce a bias. This paper proposes a local rational modelling technique which completely removes the bias from the estimation procedure and is applicable to large-scale multiple-input, multiple-output systems. The developed technique uses the bootstrapped total least squares estimator which provides unbiased estimates for the local rational model and generates accurate uncertainty bounds for the obtained non-parametric frequency response function. The proposed technique is successfully verified using a simulation example of a large-scale system which contains 100 resonances and has 100 inputs and 100 outputs. Its practical applicability is illustrated by characterising the resonant behaviour of the tailplane of a glider.

[1]  Tae Tom Oomen,et al.  Non-parametric identification of multivariable systems: A local rational modeling approach with application to a vibration isolation benchmark , 2018 .

[2]  Julius S. Bendat,et al.  Engineering Applications of Correlation and Spectral Analysis , 1980 .

[3]  Tom Dhaene,et al.  Iterative rational least-squares method for efficient transfer function synthesis , 2006 .

[4]  Yves Rolain,et al.  Analyses, development and applications of TLS algorithms in frequency domain system identification , 1997 .

[5]  Sabine Van Huffel,et al.  Total least squares problem - computational aspects and analysis , 1991, Frontiers in applied mathematics.

[6]  Rik Pintelon,et al.  System Identification: A Frequency Domain Approach , 2012 .

[7]  Rik Pintelon,et al.  FRF Measurement of Nonlinear Systems Operating in Closed Loop , 2013, IEEE Transactions on Instrumentation and Measurement.

[8]  D. J. Ewins,et al.  Modal Testing: Theory and Practice , 1984 .

[9]  J. Schoukens,et al.  Parametric identification of transfer functions in the frequency domain-a survey , 1994, IEEE Trans. Autom. Control..

[10]  Gerd Vandersteen,et al.  Improved FRF Measurements of Lightly Damped Systems Using Local Rational Models , 2018, IEEE Transactions on Instrumentation and Measurement.

[11]  P. Guillaume,et al.  Constrained maximum likelihood modal parameter identification applied to structural dynamics , 2016 .

[12]  Paul Sas,et al.  Modal Analysis Theory and Testing , 2005 .

[13]  T. McKelvey,et al.  Non-parametric frequency response estimation using a local rational model , 2012 .

[14]  Rik Pintelon,et al.  Asymptotic Uncertainty of Transfer-Function Estimates Using Nonparametric Noise Models , 2007, IEEE Transactions on Instrumentation and Measurement.

[15]  C. Paige Computing the generalized singular value decomposition , 1986 .

[16]  J. Schoukens,et al.  Estimation of nonparametric noise and FRF models for multivariable systems—Part I: Theory , 2010 .

[17]  Tae Tom Oomen,et al.  A local rational model approach for H ∞ norm estimation:With application to an active vibration isolation system , 2017 .

[18]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[19]  C. Sanathanan,et al.  Transfer function synthesis as a ratio of two complex polynomials , 1963 .

[20]  Tom Dhaene,et al.  Vector Fitting vs. Levenberg-Marquardt : Some experiments , 2009, 2009 IEEE Workshop on Signal Propagation on Interconnects.

[21]  Rik Pintelon,et al.  A Gauss-Newton-like optimization algorithm for "weighted" nonlinear least-squares problems , 1996, IEEE Trans. Signal Process..

[22]  Jorge J. Moré,et al.  The Levenberg-Marquardt algo-rithm: Implementation and theory , 1977 .

[23]  David R. Brillinger,et al.  Time Series: Data Analysis and Theory. , 1982 .

[24]  Yves Rolain,et al.  Bounding the Polynomial Approximation Errors of Frequency Response Functions , 2013, IEEE Transactions on Instrumentation and Measurement.

[25]  J. Schoukens,et al.  Estimation of nonparametric noise and FRF models for multivariable systems—Part II: Extensions, applications , 2010 .

[26]  Yves Rolain,et al.  Numerically robust transfer function modeling from noisy frequency domain data , 2005, IEEE Transactions on Automatic Control.

[27]  Peter Avitabile,et al.  Comparison of Modal Parameters Extracted Using MIMO, SIMO, and Impact Hammer Tests on a Three-Bladed Wind Turbine , 2014 .

[28]  J. Willems,et al.  Parametrizations of linear dynamical systems: Canonical forms and identifiability , 1974 .

[29]  Joe Brewer,et al.  Kronecker products and matrix calculus in system theory , 1978 .