Surface failure detection for an F/A-18 aircraft using neural networks and fuzzy logic
Ioannou, Petros A.
Youssef, H. M.
SourceIEEE International Conference on Neural Networks - Conference Proceedings
Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
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In this paper we consider the problem of detecting control surface failures of a high performance aircraft. The detection model is developed using a linear, six degree of freedom dynamic model of an F/A-18 aircraft. The detection scheme makes use of a residual tracking error between the actual system and the model output in order to detect and identify a particular fault. Two parallel models detect the existence of a surface failure, whereas the isolation and magnitude of any one of the possible failure modes is estimated by a decision algorithm using either neural networks or fuzzy logic. Simulation results demonstrate that detection can be achieved without false alarms even in the presence of actuator/sensor dynamics and noise.