Identification of finite impulse response models: Methods and robustness issues

In model predictive control one often needs a finite impulse response (FIR) or step response model of the process. Several algorithms have been proposed for the direct identification of these nonparsimonious models (least-squares and biased algorithms such as regularized least squares and partial least squares). These algorithms are compared from several points of view: the closeness of the fit to the true model, the level of robust stability …