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Piloted simulation and e ight-test results from the evaluation of a recone gurable e ight control law for the X-36 tailless e ghter aircraft are presented. The recone gurable control law, which is based on dynamic inversion in an explicitmodelfollowing framework, employs an onlineneural network to adaptively regulatetheerror in the plant inversion, which may be due to modeling uncertainties, failures, or damage. Simulated actuator failures, which causedeffectorstobelockedatprescribed positions,wereinjectedtoevaluatethestability andhandling qualitiesof therecone gurablecontrol law underfailures. Thecontrol law was not given any knowledgeof the actuator failure, and fault detection logic was not employed. Piloted simulation testing was used to compare the recone gurable control law to the X-36 dynamic inversion control laws under simulated actuator failures. The recone gurable control law showed improved handling qualities and departure resistance relative to the baseline X-36 control laws.
Brinker et al. (Sat,) studied this question.