Subspace algorithms for the identification of multivariable dynamic
errors-in-variables models
C. T. Chou and Michel Verhaegen
Abstract
We consider the problem of identifying multivariable
finite dimensional linear time-invariant systems from noisy
input/output measurements.
Apart from the fact that both the measured input
and output are corrupted by additive white noise,
the output may also be contaminated by a term which is
caused by a white input process noise; furthermore all these
noise processes are allowed to be correlated with each other.
We shall develop a solution to this problem in the
framework of subspace identification and we shall show that
our algorithms give consistent estimates when the system is
operating in open- or closed-loop.
Two realistic simulation studies are presented
to demonstrate the practical applicability of the proposed algorithms.
Keywords
System identification; errors-in-variables; subspace methods;
instrumental variable methods; consistency; closed-loop
identification; state space models.