
System identification is a procedure by which a mathematical
description of vehicle or component dynamic behavior
is extracted from test data. System identification
can be thought of as an inverse of simulation. Simulation
requires the adoption of (a-priori) engineering assumptions
to allow the formulation of model equations. These
simulation models are then used to predict aircraft
or subsystem motion. In contrast, system identification
begins with measured aircraft motion and "inverts" the
responses to rapidly extract a model which accurately
reflects the measured aircraft motion, without making
a-priori assumptions or requiring a time-consuming
modeling effort. Applications of system identification
results include: (1) comparison of wind tunnel and
flight characteristics; (2) validation and update of
simulation models; (3) handling-qualities analyses
and specification compliance; (4) optimization of automatic
flight control systems; and (5) vibration and aeroelastic
analyses.
The U.S. Army and University of California, Santa Cruz
(UARC) have jointly developed an integrated facility
for system identification based on a comprehensive
frequency-response approach that is uniquely suited
to the difficult problems associated with flight test
data analysis. The foundation of the CIFER approach
is the high-quality extraction of a complete multi-input/multi-output
(MIMO) set of non-parametric input-to-output frequency
responses. These responses fully characterize the coupled
characteristics of the system without a-priori assumptions.
Advanced Chirp-Z transform and composite optimal window
techniques developed and exercised with over 10 years
of flight project applications provide significant
improvement in frequency-response quality relative
to standard Fast Fourier Transforms (FFTs). Sophisticated
nonlinear search algorithms are used to extract a state-space
model which matches the complete input/output frequency-response
data set.
Key features of the CIFER® approach are:
Application modules within CIFER allow the:

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