Abstract
This paper focuses on the identification problem of multivariable controlled autoregressive autoregressive (CARAR-like) systems. The corresponding identification model contains a parameter vector and a parameter matrix, and thus the conventional least squares methods cannot be applied to directly estimate the parameters of the systems. By using the hierarchical identification principle, this paper presents a hierarchical generalized least squares algorithm and a filtering based hierarchical least squares algorithm for the multivariable CARAR-like systems. The simulation results show that the two hierarchical least squares algorithms are effective.
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