Evaluation of a Fault-Tolerant Scheme in a Six-Degree-of-Freedom Motion Flight Simulator

Abstract

This paper discusses the evaluation of a neurally augmented fault-tolerant flight control scheme for a high-performance military aircraft featuring an adaptive actuator and sensor failure detection, isolation, and identification algorithm in a motion-based flight simulator. The design of the fault-tolerant control scheme is based on a nonlinear dynamic inversion schemewithaneuralnetwork-basedaugmentationforreducingthedynamicinversionerrors associated with the occurrence of an actuator failure while a set of online learning neural observers is used for dealing with specific sensor failures. The failure detection, isolation, and identification scheme is based on an adaptive threshold technique for estimating failure bounds associated with both actuator and sensor failures. Also, an ‘ad-hoc’ parameter is proposed here for the novel task of evaluating the pilot workload in compensating for both actuatorandsensorfailuresonboardtheaircraft.Ageneraloutcomeoftheeffortisademonstration of the importance of realistic motion-based simulation environments for evaluation of this specific class of flight control laws. The study also demonstrated the importance of the neural augmentation for failure accommodation purposes and the effectiveness of the proposed adaptive threshold technique for failure identification purposes.