An Iterative Method to Stabilize a Transfer Function in the$s$- and$z$-Domains

Stability of the identified model is crucial in simulations and prediction applications. This paper proposes a two-step procedure that generates guaranteed stable transfer function (matrix) models from noisy data. It consists of an unconstrained optimal noise removal step followed by a stable approximation of the possible unstable estimate. The proposed method is applicable to continuous-time as well as discrete-time and multivariable systems