Expert supervision of fuzzy learning systems for fault tolerant aircraft control

In this paper, we begin by showing that the fuzzy model reference learning controller (FMRLC) can be used to reconfigure the nominal controller in an F-16 aircraft to compensate for various actuator failures without using explicit failure information. Next, we show that the performance of the FMRLC can be significantly enhanced by exploiting failure detection and identification (FDI) information to achieve a “performance adaptive” 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 at which a failure occurs and show that it achieves higher performance control reconfiguration than an unsupervised FMRLC. In addition we show that similar performance can be achieved if we only use estimates of the 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