For Release: Nov. 12, 1996
Headquarters, Washington, DC
Langley Research Center, Hampton, Va.
Robert M. Pap
Accurate Automation, Chatanooga, Tenn.
RELEASE NO. 96-173 (Headquarters Rel. 96-233)
Aircraft Flight Control System Wins "Best of What's
An experimental aircraft flight control system that learns as it
flies has been honored as one of the best technology developments
Developed for NASA and the U.S. Air Force, the computerized
flight control system has been installed on an 8-foot-4-inch
unpiloted aircraft called "LoFlyte" being prepared
for flight demonstrations this month. The jet-powered aircraft was
developed by Accurate Automation Corp., Chattanooga, TN, under the
Small Business Innovation Research program.
The LoFlyte hypersonic waverider aircraft was named one of the
100 "Best of What's New" in the annual Popular Science magazine
competition. Winners were announced Nov. 12 at an exhibition in New
York City's Central Park and will be featured in the magazine's
Individuals and organizations cited for their work with LoFlyte
are James L. Hunt, NASA Langley Research Center, Hampton, Va.; Dr.
Kervyn Mach, Air Force Wright Laboratory in Dayton, Ohio; and Bob
Pap, Accurate Automation.
The flights are taking place at Edwards, Calif., with the
support of NASA Dryden Flight Research Center.
The experimental LoFLYTE aircraft will be used to explore new
flight control techniques involving neural networks, which will
allow an aircraft control system to learn by mimicking the pilot.
Technologies being implemented in the LoFLYTE program could
eventually find their way into commercial, general aviation and
The model is a Mach 5 waverider concept, a futuristic hypersonic
aircraft configuration that could cruise on top of its own
shockwave if powered to hypersonic speeds. Waverider aircraft,
powered by airbreathing hypersonic engines, would fly at speeds
above Mach 4. LoFLYTE represents the first known flying waverider
vehicle configuration. In the current flight tests it will be
powered by a small-scale jet engine and will reach subsonic speeds
to explore take-off and landing control issues.
The aircraft has been designed to demonstrate that neural
network flight controls are superior to conventional flight
controls. Neural networks are computer systems that actually learn
by doing. The computer network consists of many interconnected
control systems, or nodes, similar to neurons in the brain. Each
node assigns a value to the input from each of its counterparts. As
these values are changed, the network can adjust the way it
The LoFLYTE aircraft's flight controller consists of a network
of multiple-instruction, multiple-data neural chips. The network
will be able to continually alter the aircraft's control laws in
order to optimize flight performance and take the pilot's responses
into consideration. Over time, the neural network system could be
trained to control the aircraft. The use of neural networks in
flight would help pilots of future aircraft to fly in
quick-decision situations and help damaged aircraft land safely
even when controls are partially disabled.
The waverider was chosen as the testbed for the neural networks
because the configuration has an inherently high hypersonic
lift-to-drag ratio at hypersonic speeds. If neural networks can
control this "worst-case scenario" configuration, then they should
be able to handle any other desired configuration, project
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