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This paper derives state-space models for multirate multi-input sampled-data systems. Based on the corresponding transfer function models, an auxiliary model based recursive least squares algorithm is presented to identify the model parameters of the multirate systems from the multirate input-output data. Further, convergence properties of the proposed algorithm are analyzed. An illustrative example...
This paper studies identification problems for two-input multirate systems with colored noises (The method in the paper can be easily extended to multi-input multirate systems). The state-space models are derived for the multirate systems with two different input sampling periods and furthermore the corresponding transfer functions are obtained. To solve the difficulty of identification models with...
In order to reduce computational burden of identification methods for multivariable systems, a hierarchical least squares (HLS) algorithm is developed. The basic idea is to use the hierarchical identification principle to decompose the identification model of the multivariable system into several submodels with smaller dimensions and fewer variables, and then to identify the parameter vector of each...
This paper uses the lifting technique to derive the lifted state-space models for non-uniformly sampled multirate systems, and transform the obtained models into the canonical ones. Based on the Kalman filtering principle, we derive the state filtering algorithm by minimizing the estimation error covariance matrix and further compute the state estimates of the original systems by using inverse transformation...
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