Actuator sensor or other aircraft subsystem failures, or structural fa
ilures that result from, for example, battle damage can cause catastro
phes that may lead to loss of the aircraft. While experienced pilots c
an often compensate for failures, in certain emergency situations ther
e is the need for computer-assisted or fully computer-automated reconf
iguration of the aircraft control laws to save the aircraft. In this p
aper, we begin by showing that the fuzzy model reference learning cont
roller (FMRLC) [1]-[5] can be used to reconfigure the nominal controll
er in an F-16 aircraft to compensate for various actuator failures wit
hout using explicit failure information (e.g., the time of occurrence
of the failure or its magnitude). Next, we show that the performance o
f the FMRLC can be significantly enhanced by exploiting failure detect
ion and identification (FDI) information to achieve a ''performance ad
aptive'' system that seeks an appropriate performance level depending
on the type of failure that occurred We develop an expert supervision
strategy for the FMRLC that uses only information about the time al wh
ich a failure occurs and show that it achieves higher performance cont
rol reconfiguration than an unsupervised FMRLC. In addition, we show t
hat similar performance can be achieved if we only use estimates of th
e failure time and magnitude obtained from a fuzzy estimator We close
our study with a brief assessment of the advantages and disadvantages
of the approaches used in this paper.