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Continuous-time predictor-based subspace identification using Laguerre filters

Continuous-time predictor-based subspace identification using Laguerre filters

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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.

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