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Frequency domain subspace identification algorithms have been studied recently by several researchers in the literature, motivated by the significant development of the more popular time domain counterparts. Usually, this class of methods are focused on discrete-time models, since in the case of continuous-time models, the data matrices often become ill-conditioned if we simply rewrite the Laplace operator s as s = jw, where w denotes the frequency. This paper proposes an efficient and convenient approach to frequency domain subspace identification for continuous-time systems. The operator w = (s a)/(s + a) is introduced to avoid the ill-conditioned problem. Hence the system can be identified based on a state-space model in the w-operator. And then the estimated w-operator state-space model can be transformed back to the common continuous-time state-space model. An instrumental variable matrix in the frequency domain is also proposed to obtain consistent estimates of the equivalent system matrices in the presence of measurement noise. Simulation results are included to verify the efficiency of the proposed algorithms.

Frequency Domain Subspace Model Identification with the Aid of the &omega;-Operator

Frequency Domain Subspace Model Identification with the Aid of the ω-Operator