On simplification of Dual-Youla approach for closed-loop identification

TL;DR

This paper simplifies the dual Youla approach for closed-loop system identification by removing the need for coprime factorization, maintaining its advantages and aligning it with the stabilized prediction error method, supported by simulation results.

Contribution

It introduces a simplified dual Youla method that directly identifies the plant without coprime factorization, preserving robustness and accuracy.

Findings

The simplified method retains robustness against noise and unstable plants.

It is equivalent to the stabilized prediction error method.

Simulation results confirm the method's effectiveness.

Abstract

The dual Youla method for closed loop identification is known to have several practically important merits. Namely, it provides an accurate plant model irrespective of noise models, and fits inherently to handle unstable plants by using coprime factorization. In addition, the method is empirically robust against the uncertainty of the controller knowledge. However, use of coprime factorization may cause a big barrier against industrial applications. This paper shows how to derive a simplified version of the method which identifies the plant itself without coprime factorization, while enjoying all the merits of the dual Youla method. This simplified version turns out to be identical to the stabilized prediction error method which was proposed by the authors recently. Detailed simulation results are given to demonstrate the above merits.