Identification of time-varying linear models

This paper presents a convergence analysis of a least squares type recursive identification algorithm for time-varying linear-in-the parameters models. It is shown that under persistent excitation conditions, the number of the associated gain matrix is bounded. This is obtained by normalizing the observation vector entering in the identification algorithm. This allows a simple convergence analysis to study the influence of plant parameters changes and noise in the estimates.