Modeling continuous-time processes via input-to-state filters

Authors:
Kaushik Mahata;Minyue Fu
Affiliations:
Centre for Complex Dynamic Systems and Control, School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW 2308, Australia;Centre for Complex Dynamic Systems and Control, School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW 2308, Australia
Venue:
Automatica (Journal of IFAC)
Year:
2006

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Abstract

A direct algorithm to estimate continuous-time ARMA (CARMA) models is proposed in this paper. In this approach, we first pass the observed data through an input-to-state filter and compute the state covariance matrix. The properties of the state covariance matrix are then exploited to estimate the half-spectrum of the observed data at a set of user-defined points on the right-half plane. Finally, the continuous-time parameters are obtained from the half-spectrum estimates by solving an analytic interpolation problem with a positive real constraint. As shown by simulations, the proposed algorithm delivers much more reliable estimates than indirect modeling approaches, which rely on estimating an intermediate discrete-time model.