New Closed-Loop Identification Approach Based on Output Over-Sampling Scheme

Abstract

A new identification algorithm is investigated for direct closed-loop identification by using the cyclostationarity of output over-sampled data. It has been shown that the plant model can be directly identified from the input and output data in the output over-sampling scheme even less excitation is available in the test input. However, the numerical optimization in conventional direct algorithms ordinarily depends on the initial values and estimation of noise process, whereas the estimation accuracy of the noise model is fragile to the poles and zeros of its transfer function. The properties of instinct cyclostationarity and the associated subspace characteristics of the sampled data in the output over-sampling scheme are analyzed in the paper. It illustrates that these properties can be applied for identification, and can reduce the influence of sensitivity to the noise model estimation and initial values in the numerical optimization. The simulation examples illustrate that the proposed algorithm can significantly improve the identification performance in direct closed-loop identification.