Performance enhancement of a multiple model adaptive estimator
Maybeck, P.S.; Hanlon, P.D.
Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on
Volume , Issue , 15-17 Dec 1993 Page(s):462 - 468 vol.1
Digital Object Identifier 10.1109/CDC.1993.325104
Summary:This paper describes various performance improvement techniques
for a multiple model adaptive estimator (MMAE) used to detect and
identify control surface and sensor failures on an unmanned research
flight vehicle. The MMAE uses a bank of Kalman filters that predict the
aircraft response to a given input, with each model based on a different
failure hypothesis, and then forms the residual difference between the
predicted and actual sensor measurements for each filter. The MMAE uses
these residuals to determine the probabilities of the failures that are
modeled by each of the Kalman filters. Initially the MMAE identified
most failures within one second and all within four seconds of onset,
but with various performance improvement techniques, the identification
time was reduced to less than two seconds. The techniques that will be
described are removal of “β dominance” effects,
bounding the hypothesis conditional probabilities, retuning the Kalman
filters, increasing the scalar penalty for measurement residuals,
decreased probability smoothing, and increased residual propagation. The
noted performance improvement was mostly due to removing the
“β dominance” effects, lower bounding the hypothesis
conditional probabilities, increasing the scalar penalty for measurement
residuals, and retuning of the Kalman filters
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