Identification of continuous systems using digital low-pass filters

Abstract

Direct methods for recursive identification of continuous systems from sampled input–output data using digital low–pass filters are discussed. The digital low–pass filters are introduced to avoid direct approximations of system signal derivatives from sampled data. Using a pre–designed digital low–pass filter, an approximated discrete–time estimation model is constructed easily. Thus the system parameters can be identified directly by recursive identification algorithms. Numerical results, show that the parameter estimates are not so sensitive to the pass-band of the filter, and if the filter is designed so that its pass-band matches that of the system closely and thus the noise effects are sufficiently reduced, accurate estimates can be obtained by recursive identification algorithms. Two classes of filters (FIR digital filter and IIR digital filter) are considered. It is shown that some other methods can be unified to be either the IIR or the FIR filtering approach.