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.
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 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.
Technologies being implemented in the LoFLYTE program could eventually find their way into commercial, general aviation and military aircraft.
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