Autorotation of an UAV helicopter via reinforcement learning = 강화학습을 이용한 무인 헬리콥터 오토로테이션

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 400
  • Download : 0
The single-engine manned helicopters can face the undesired engine failure during their mission operation. In this case the pilots typically perform the autorotation maneuver properly and achieve the safe landing. For an unmanned air vehicle (UAV) helicopter, construction of the controller performing the autorotation maneuver autonomously is considered as a significant subject. It allows the user to save not only the UAV helicopter platform but the valuable sensors and data on board. However, the autorotation maneuver is a quite challenging problem that requires time-critical control inputs to accomplish a safe landing. Also, the dynamics and aerodynamics of the helicopter in autorotation are highly non-linear and complex. From these facts it is necessary to devise a controller which is able to obtain satisfactory performance in this field. Reinforcement learning is the feasible approach that can be applied in a complicated system. It is known that the reinforcement learning technique is based on the way of animal’s thinking, learning and acting. Contrary to supervised learning, the controller can adapt itself by the suggested reward function in a natural way. After several trials, the controller can find the solution to success the safe landing via reinforcement learning.
Advisors
Bang, Hyo-Choongresearcher방효충researcher
Description
한국과학기술원 : 항공우주공학전공,
Publisher
한국과학기술원
Issue Date
2008
Identifier
297281/325007  / 020063370
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 항공우주공학전공, 2008.2, [ vii, 58 p. ]

Keywords

reinforcement learning; autorotation; UAV helicopter; 강화학습; 오토로테이션; 무인 헬리콥터; reinforcement learning; autorotation; UAV helicopter; 강화학습; 오토로테이션; 무인 헬리콥터

URI
http://hdl.handle.net/10203/27027
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=297281&flag=dissertation
Appears in Collection
AE-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0