CIFER®-MATLAB Interfaces: Development and ... - Cal Poly

secondary off-axis control inputs. This coupling has significant potential to distort the response
identification of the system. CIFER ® addresses this issue with a program called MISOSA or
Multi-Input / Single-Output Spectral Analysis.
MISOSA analysis on the user level is very straightforward. The primary control of interest is
specified along with any other controls that are to be considered secondary. The program then
performs spectral analysis to remove the effects of the secondary controls from the primary
control response. It uses a matrix inversion of the input autospectrum at each frequency point to
accomplish this. This general form is shown in Equation 2.4. G xx is the matrix of auto and crossspectra
for the inputs and G xy is a vector containing the cross-spectra for each control input and
the single output in question.
T
−1
( f ) G ( f ) G ( f )
= [2.4]
xx
There are much fewer screens that drive MISOSA compared to FRESPID. The primary
information needed is details about where the frequency responses are stored (whether in a file or
in the database), names for the controls and outputs, and the desired combinations of responses to
calculate. There is no conditioning or windowing of data involved, which results in less error
checking.
xy
2.3 Combining Windows – COMPOSITE
Normally the optimization of window sizes in the FFT calculation would be a very time
consuming process. For a four-input, nine-output system there would be thirty-six responses that
would each have to be individually optimized. CIFER ® employs COMPOSITE (for composite
windowing) to combine the individual windowed responses from FRESPID into a single
combined response, thus automating the optimization. It uses a nonlinear, least-squares
optimization of a cost function based on the auto and cross-spectra to combine the window data.
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