SVD-based subspace methods for multivariable continuous-time systems identification

Abstract

Recently severed subspace methods have appeared in the literature for multivariable discrete-time state space identification, where state space models are computed directly from input/output data. These state space identification methods are viewed as the better alternatives to polynomial model identification, owing to the better numerical conditioning associated with state space models, especially for high-order multivariable systems.

In this contribution, a similar method is described for continuoustime state space identification. Here also, the key tool is the singular value decomposition (SVD), a numerical technique known to be very robust and accurate when dealing with noisy data. The noise coloring is compensated for by using a generalization of the SVD, namely the quotient SVD. The resulting identification scheme is then shown to give consistent results under certain conditions.