AIRCRAFT FLIGHT CONTROL SYSTEM WINS "BEST OF WHAT’S NEW"
Tue, 12 Nov 1996 16:01:38 -0500
Source: NASA HQ Public Affairs Office
Don Nolan-Proxmire
Headquarters, Washington, DC
Nov. 12, 1996
(Phone: 202/358-1983)
Keith Henry
Langley Research Center, Hampton, VA
(Phone: 804/864-6120)
RELEASE: 96-233
AIRCRAFT FLIGHT CONTROL SYSTEM WINS "BEST OF WHAT'S NEW"
An experimental aircraft flight control system that
learns as it flies has been honored as one of the best
technology developments of 1996.
Developed for NASA and the U.S. Air Force, the
computerized flight control system has been installed on an
8-foot-4-inch remotely piloted 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 today at an exhibition
in New York City's Central Park and will be featured in the
magazine's December issue.
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 remotely-
sited pilot. Technologies being implemented in the LoFLYTE
program could eventually find their way into commercial,
general aviation and military aircraft. Flights are taking
place at Edwards,CA, with the support from NASA¹s
Dryden Flight Research Center.
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 reach hypersonic speeds.
LoFLYTE represents the first known flying waverider vehicle
configuration. In the current flight tests it is powered by a
small-scale jet engine and will reach subsonic speeds to
explore takeoff 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 responds.
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 could 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 lift-to-drag ratio at hypersonic speeds. If neural
networks can control this "worst-case scenario"
configuration, then they should be able to handle virtually
any conventional configuration, project officials say.
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
Dayton, OH; and Bob Pap, Accurate Automation.
- end -
All rights reserved to WUFOC and NÄRKONTAKT. If you reprint or quote any part of the content,
you must give credit to: WUFOC, the free UFO-alternative on the Internet, http://www.wufoc.com