Investigation of Deep Reinforcement Learning for Longitudinal-Axis Flight Control-Reference-Cited by-同舟云学术

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Investigation of Deep Reinforcement Learning for Longitudinal-Axis Flight Control

Published:2024-03 Issue:2 Volume:37 Page:
ISSN:0893-1321
Container-title:Journal of Aerospace Engineering
language:en
Short-container-title:J. Aerosp. Eng.

Author:

Xu Ke¹^ORCID,Tian Shuling²,Xia Jian³^ORCID

Affiliation:

1. Ph.D. Student, Key Laboratory of Unsteady Aerodynamics and Flow Control, Ministry of Industry and Information Technology, Nanjing Univ. of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China. ORCID: .

2. Associate Professor, Key Laboratory of Unsteady Aerodynamics and Flow Control, Ministry of Industry and Information Technology, Nanjing Univ. of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China (corresponding author).

3. Professor, Key Laboratory of Unsteady Aerodynamics and Flow Control, Ministry of Industry and Information Technology, Nanjing Univ. of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

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

Link

https://ascelibrary.org/doi/pdf/10.1061/JAEEEZ.ASENG-5007

Reference39 articles.

1. Aggarwal, S. 2002. System identification of X33 using neural network. The 2002 NASA Faculty Fellowship Program Research Reports. Daytona Beach, FL: Embry Riddle Aero Univ.

2. Blazek, J. 2001. Computational fluid dynamics: Principles and applications. Amsterdam, Netherlands: Elsevier.

3. Considerations on the spring analogy

4. Machine Learning for Fluid Mechanics

5. Sensor hybridization using neural networks for rocket terminal guidance

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