PMF-Based Subspace Method for Continuous-Time Model Identification Deterministic Study

Abstract This paper presents the use of the Poisson Moment Functionals (PMF) combined with Subspace based State-Space System IDentification (4SID) methods for continuous-time state-space model identification directly from sampled I/O data. The main purpose of this study is to show the PMF order limits and their consequences as deterministic errors on the estimation results. In particular, it seems that the 4SID estimation step of matrices B and D is the most sensitive to these deterministic disturbances. A double filtering technique is introduced by using a reduced order filter before estimating B and D in order to keep constant the estimation performances.