Reinforcement Learning for Helicopter Flight Control

Master Thesis (2020)
Author(s)

B. Helder (TU Delft - Aerospace Engineering)

Contributor(s)

Erik-Jan van Kampen – Mentor (TU Delft - Control & Simulation)

M.D. Pavel – Graduation committee member (TU Delft - Control & Simulation)

Faculty
Aerospace Engineering
Copyright
© 2020 Bart Helder
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 Bart Helder
Graduation Date
30-04-2020
Awarding Institution
Delft University of Technology
Programme
['Aerospace Engineering']
Faculty
Aerospace Engineering
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Abstract

Large-scale helicopters have unique characteristics of maneuverability and low-speed performance compared to fixed-wing aircraft. They can take off and land vertically, hover in place for extended periods of time, and move in all six directions, making them occupy important niches in both military and civil aviation. However, these advantages come at a cost: helicopters are inherently unstable with complicated dynamics, and generally more unsafe than commercial air travel. The fatality rate of non-military helicopters is about 1.44 per 100,000 ight hours [9]. This high number is partially explained by the more risky nature of helicopter missions, but is still considerably high compared to the fatality rate of commercial aviation in general.

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