Abstract
In this paper, we study the problem of the model structure validation for closed-loop system identification. Two probabilistic model uncertainties and optimum input filter are derived from some statistical properties of the parameter estimation. The probabilistic bounds and optimum input filter are based on an asymptotic normal distribution of the parameter estimator and its covariance matrix, which was estimated from sampled data. The uncertainties bounds of the model parameter and cross-correlation function are constructed in the probability sense by using the inner product form of the asymptotic covariance matrix. Further, the input filter is derived from the point of optimisation. Finally, the simulation example results confirm the identification theoretical results.
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