Closed-loop subspace identification via generalized Possion moment functionals
To solve the problem of biased estimation due to the correlation between future input and noise in the identification process of continuous system,a closed-loop subspace identification algorithm utilizing generalized Possion moment functionals is presented.Firstly,the filter model of the input-output signals is obtained by generalized Possion moment functionals transformation,and then the input-output matrix equation of the continuous systems is obtained.Secondly,instead of using the observable subspaces in the process of subspace identification,this paper focus on the parity space which is commonly employed in fault detection.Finally,the system is estimated consistently by the principal component analysis and instrumental variable method,which solves the identification problems of biased results for the system operates in closed-loop identification due to the feedback controller.The effectivity and accuracy of the proposed method are verified by the simulation results.
subspace identificationclosed-loop identificationgeneralized Possion moment functionals(GPMF)parity space
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