On the estimation of continuous time transfer functions

Abstract

This paper proposes a state variable filter approach to continuous time system identification. Two topics are studied in the paper. The first topic is related to the choice of state variable filters. The strategy we adopt is to adjust the time constants of the state variable filters so that a prediction error criterion is minimized. As a result, the estimated model reaches a balance between bias and variances shown by a simulation example. The second topic is related to the choice of model structure. We extend a multiple model estimation algorithm, developed using UD factorization, to continuous time sysem identification. The estimation algorithm generates a set of candidate models, among wh..