Don Nolan-Proxmire
Headquarters, Washington, DC                   August 2, 1996
(Phone:  202/358-1983)

Mary M. Spracher
Langley Research Center, Hampton, VA
(Phone:  757/864-6527/6120)

Robert M. Pap
Accurate Automation Corp., Chattanooga, TN
(Phone: 423/894-4646)

RELEASE:  96-154

INTELLIGENT TEST AIRCRAFT UNVEILED IN WISCONSIN TODAY

     NASA and the U.S. Air Force today unveiled a jet-powered 
aircraft equipped with state-of-the-art flight control 
technologies that will demonstrate a computerized flight 
control system that learns as it flies -- especially important 
for the demands of ultra high-speed flight.

     Called the Low-Observable Flight Test Experiment 
(LoFLYTE), the 8-foot-4-inch aircraft, announced at a briefing 
in Oshkosh, WI, has been developed by Accurate Automation 
Corp., Chattanooga, TN, for NASA and the Air Force. The 
program contracts are being administered through NASA's 
Langley Research Center, Hampton, VA, and the Air Force Wright 
Laboratory, Dayton, OH, under the Small Business Innovative 
Research Program.

     The experimental LoFLYTE aircraft will be used to explore 
new flight control techniques involving neural networks, which 
allow the aircraft control system to learn by mimicking the 
pilot. 

     The model is a Mach 5 waverider design -- a futuristic 
hypersonic aircraft configuration that actually cruises on top 
of its own shockwave.  Waverider aircraft, powered by 
airbreathing hypersonic engines, would fly at speeds above 
Mach 4. LoFLYTE represents the first known flying waverider 
vehicle configuration, but upcoming flight tests at NASA's 
Dryden Flight Research Center, Edwards, CA, will be flown only 
at low subsonic speeds to explore take-off and landing control 
issues.

     The remotely-piloted aircraft has been designed to 
demonstrate that neural network flight controls are superior 
to conventional flight controls. 

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                         -2-

      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 responds.

     The 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 fly in quick-
decision situations and help damaged aircraft land safely even 
when controls are partially destroyed.

     The main objective of LoFLYTE is to demonstrate the 
utility of such a flight control system that learns through 
experience, said Robert Pegg of Langley's Hypersonic Vehicles 
Office. In addition to experimenting with neural networks, the 
flight of the model also is key as a low-speed demonstration 
of a hypersonic vehicle. "We're very interested in both 
outcomes, both the neural net technology and the flight 
characteristics," he said.

     "We see a big advantage to using this type of control 
system in a hypersonic vehicle," Pegg said. "At those high 
speeds, things happen so quickly that the pilot cannot control 
the aircraft as easily as at subsonic speeds."

     The initial configuration for the aircraft was developed 
at Langley. Accurate Automation Corp. will then integrate the 
neural network technology into the Langley design. Successful 
tests of the waverider concept in Langley's 12-foot Low-Speed 
Wind Tunnel and 30- by 60-foot Full Scale Tunnel preceded the 
development of this model aircraft.

     The construction of the model was completed at SWB 
Turbines, Appleton, WI. The company also provided the small 
turbine engine that powers the model. The shell of the model 
was built at Mississippi State's Raspet Flight Research 
Laboratory and then shipped to SWB Turbines so that the radio 
control gear and the engine could be installed.

     The waverider was chosen as the testbed for the neural 
networks because the configuration has an inherently high 
hypersonic lift-to-drag ratio. If neural networks can control 
this "worst-case scenario" configuration, then they should be 
able to handle virtually any other configuration. The 
waverider configuration was also chosen because it allows for 
long hypersonic cruise ranges of up to 8,000 miles. At an 
altitude of 90,000 feet, a Mach 5 waverider would fly at a 
rate of one mile per second.

     "We want to make the public aware that the government is 
getting a good return on its SBIR-invested money," Pegg said. 
"We hope this project will help us further demonstrate to the 
public that the SBIR program is a viable investment for the 
American people."
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     Technologies being implemented in the LoFLYTE program 
could  eventually find their way into commercial, general 
aviation and military aircraft.

                        - end -

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