Attitude Control of a Moving Mass–Actuated UAV Based on Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Attitude Control of a Moving Mass–Actuated UAV Based on Deep Reinforcement Learning

Published:2022-03 Issue:2 Volume:35 Page:
ISSN:0893-1321
Container-title:Journal of Aerospace Engineering
language:en
Short-container-title:J. Aerosp. Eng.

Author:

Qiu Xiaoqi¹^ORCID,Gao Changsheng²,Wang Kefan³,Jing Wuxing⁴

Affiliation:

1. Ph.D. Candidate, School of Astronautics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China. ORCID: .

2. Professor, School of Astronautics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China (corresponding author).

3. Ph.D. Candidate, School of Astronautics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.

4. Professor, School of Astronautics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Aerospace Engineering,General Materials Science,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29AS.1943-5525.0001381

Reference42 articles.

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2. Bou-Ammar H. H. Voos and W. Ertel. 2010. “Controller design for quadrotor UAVs using reinforcement learning.” In Proc. 2010 IEEE Int. Conf. on Control Applications 2130–2135. New York: IEEE.

3. Calhoun P. and E. Queen. 2002. “Entry vehicle control system design for the Mars smart lander.” In Proc. AIAA Atmospheric Flight Mechanics Conf. and Exhibit. San Diego: American Institute of Aeronautics and Astronautics.

4. Composite Control of Stratospheric Airships with Moving Masses

5. A novel concept of VTOL bi-rotor UAV based on moving mass control

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