Autonomous Autorotation of an Unmanned Helicopter Using a Reinforcement Learning Algorithm

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Single-engine manned helicopters can face unexpected engine failure during their mission operations. In this situation, pilots typically perform an autorotation maneuver to a safe landing, which demands great skill on the part of the pilot. Similarly, unmanned helicopters can encounter engine failure during autonomous operations. Typically, an autorotation maneuver of a helicopter under engine failure can be divided into three phases. First, the entry phase consists of controlling the angular motion of the helicopter so that the main-rotor rotation decays. Second, during the steady-state descent phase, air flows upward through the rotor disk. Potential energy of the helicopter is converted to kinetic energy to attain desired steady-state descent airspeed below the maximum sink rate. In the final phase, the airspeed and the sink rate are reduced just before touchdown.
Publisher
AMER INST AERONAUTICS ASTRONAUTICS
Issue Date
2013-02
Language
English
Article Type
Article
Citation

JOURNAL OF AEROSPACE INFORMATION SYSTEMS, v.10, no.2, pp.98 - 104

ISSN
1940-3151
URI
http://hdl.handle.net/10203/189519
Appears in Collection
AE-Journal Papers(저널논문)
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