Continuous-time predictor-based subspace identification using Laguerre filters

This study deals with the problem of continuous-time model identification and presents two subspace-based algorithms capable of dealing with data generated by systems operating in closed loop. The algorithms are developed by reformulating the identification problem from the continuous-time model to equivalent ones to which discrete-time subspace identification techniques can be applied. More precisely, two approaches are considered, the former leading to the so-called all-pass domain by using a bank of Laguerre filters applied to the input-output data and the latter corresponding to the projection of the input-output data onto an orthonormal basis, again defined in terms of Laguerre filters. In both frameworks, the Predictor-Based Subspace Identification, originally developed in the case of discrete-time systems, can be reformulated for the continuous-time case. Simulation results are used to illustrate the achievable performance of the proposed approaches with respect to existing methods available in the literature.