Some remarks on the bias distribution analysis of discrete-time identification algorithms based on pseudo-linear regressions
Provides a corrected theoretical analysis for practitioners using PLR algorithms in system identification.
The paper corrects the frequency-domain bias analysis of pseudo-linear regression (PLR) identification algorithms, showing that PLR outperforms prediction error methods (PEM) at high frequencies.
In 1998, A. Karimi and I.D. Landau published in the journal "Systems and Control letters" an article entitled "Comparison of the closed-loop identification methods in terms of bias distribution". One of its main purposes was to provide a bias distribution analysis in the frequency domain of closed-loop output error identification algorithms that had been recently developed. The expressions provided in that paper are only valid for prediction error identification methods (PEM), not for pseudo-linear regression (PLR) ones, for which we give the correct frequency domain bias analysis, both in open- and closed-loop. Although PLR was initially (and is still) considered as an approximation of PEM, we show that it gives better results at high frequencies.