Abstract
The ability of neural networks to learn from repeated exposure to system characteristics has made them a popular choice for many applications in linear and non-linear control. In this paper, the capabilities of neural networks in detecting and accommodating control surface failures for a modified F/A-18 ‘super-manoeuverable’ fighter aircraft are examined. To detect and accommodate a failure in the thrust vectoring vane during a pitch manoeuvre, a hierarchical neuro-controller is designed using thrust vectoring, symmetric leading edge flap and the throttle. This neuro- controller is then used as the fault accommodating neuro- controller. A separate neural network is trained to detect failures in the thrust vectoring vane. The performance of the controller and fault-detection networks are verified using a numerical simulation of a longitudinal model of the aircraft.
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KrishnaKumar, K., Lattus, R. Control surface failure detection and accommodation using neuro-controllers. Neural Comput & Applic 2, 120–128 (1994). https://doi.org/10.1007/BF01415007
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DOI: https://doi.org/10.1007/BF01415007