Efficient Identification of Input Dynamics for Correlation Function-Based Subspace Identification
نویسندگان
چکیده
The methods of subspace system identification are extended to correlation function estimates, explicitly addressing the increase in computational difficulty of identifying input-tostate dynamics when correlation function estimates are used in place of input-output data for multivariable identification problems. It is shown that the regressor used to solve a common least-squares problem when identifying input-to-state dynamics is the state sequence of a dual system of dynamics that have already been estimated. A new method of computing the regressor is presented that dramatically improves the computational efficiency of estimating the inputto-state dynamics when signals of high dimension are used. A simulation example demonstrates the effectiveness of the method for both input-output data and correlation function estimates.
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