Robust or fast local polynomial method: How to choose?

Griet Monteyne, Diana Ugryumova, Gerd Vandersteen, Rik Pintelon

Research output: Chapter in Book/Report/Conference proceedingMeeting abstract (Book)


The Robust (RLPM) and Fast (FLPM) Local Polynomial
Methods were developed to find a non-parametric Frequency
Response Function (FRF) estimate [1]. In this contribution
the methods are compared and applied on experimental
data coming from a heat diffusion experiment. Both
methods assume that the excitation signal is a periodic signal
and that the transient can be approximated locally in the
frequency domain by a low-degree polynomial. FLPM also
approximates the FRF by a local polynomial with a low degree.
This approximation results in a bias error in case a
low-degree polynomial cannot approximate the FRF well.
RLPM does not approximate the FRF by a local polynomial.
Thus, using RLPM avoids this bias error due to undermodeling.
Unfortunately RLPM cannot estimate the level
of the nonlinear distortions unless data coming from at least
two experiments with uncorrelated inputs is available.
Original languageEnglish
Title of host publication31th Benelux Meeting on Systems and Control, March 27-29 2012, CenterParcs Heijderbos, Heijden, The Netherlands
Publication statusPublished - 27 Mar 2012


  • RLPM
  • FLPM
  • FRF


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