Recursive algorithms for identification of dual Youla-Kucera parametrized plant models in closed loop

Abstract

The growing interest in using dual Youla Kucera plant parametrization for modeling plant uncertainties raises the need for recursive identification algorithms dedicated to the identification of these structures in closed loop in view of developing appropriate iterative tuning and adaptive control strategies. The paper presents recursive algorithms for identification in closed loop of dual Youla-Kucera parametrized plant models. These algorithms assure global asymptotic stability in the deterministic environment and allow to obtain unbiased parameter estimation in the presence of measurement noise when the plant model is in the model set. The paper also re-visit the Hansen scheme which allows to associate open loop type recursive identification algorithms for the identification of these structures in closed loop. When the plant model is not in the model set, comparison of the various algorithms is done in terms of the bias distribution. Further comparisons and performance evaluation is provided by simulations on some relevant examples and experimental identification in closed loop of a test bench for active noise control.