System identification by spectral analysis using closed-loop process data Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/rv042w542

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  • Time series from the input and the output of a process are analyzed by spectral estimation methods to develop a system transfer function. Existing process data were used. All the published computation methods were examined. Three of these have been explained and illustrated. The three methods for computing autospectra and crossspectra have been referred to as: periodogram smoothing, averaging periodograms of segmented series, and the Blackman-Tukey method. In the first two, the Fourier coefficients are calculated directly from the data and the resulting periodograms smoothed to obtain estimates of the spectra. The Blackman-Tukey approach is based on computing the covariances from the data and then Fourier transforming the smoothed time averages. Also described here is an adaptation of the Blackman-Tukey method, which takes advantage of the fast Fourier transform. This thesis also lists the precautions necessary in planning and collecting the data so as to derive maximum benefit from spectral analysis. Mutual relationships between the various forms of the linear system equations and spectral estimates have been explored.
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