The NASA F-15 (McDonnell Douglas Corporation, St. Louis, Missouri, now The Boeing Company, Chicago, Illinois) airplane was used as a testbed to flight-test a neural network controller algorithm called 'Sigma Pi.' Two types of failures were included to demonstrate the effectiveness of the neural network controller. A simulated change in aircraft stability was achieved by changing the gain on the angle-of-attack feedback to the symmetric canard. With fixed canards, the aircraft is both statically and dynamically unstable. Angle-of-attack feedback to the canards is required for longitudinal stability. A change in control effectiveness was simulated by biasing and freezing one of the stabilator control surfaces. The flight test was performed at Mach 0.75 at an altitude of 20,000 ft with maneuvering at 3 g tracking and formation tasks. Results from flight tests show that the canard multiplier failures were less severe than predicted by the nonlinear simulation. The adaptive system seemed to provide some improvement with these failures; however, the change was less dramatic than was predicted. The stabilator failures provided a good example of an asymmetric vehicle. The neural networks provided some relief from the coupled behavior, however, with the neural networks engaged, the system tended to be much more PIO-prone in the pitch axis. Pilot control stick motions revealed that pilot compensation was adequate to deal with most of the cross coupling when accomplishing a pitch task. For task accomplishment requiring motions in the lateral axis, however, pilot compensation was less successful.


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    Title :

    F-15 Intelligent Flight Control System Neural Network Flight Systems Summary


    Contributors:
    R. Larson (author)

    Publication date :

    2007


    Size :

    2 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English





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