Suppression of Roll Oscillations of a Canard-Configuration Model Using Fluid Effector and Reinforcement Learning

Author:

Dong Yizhang1,Shi Zhiwei2,Chen Kun3,Ge Zengran4

Affiliation:

1. Ph.D. Student, College of Aerospace Engineering, Nanjing Univ. of Aeronautics and Astronautics, Nanjing 210000, China; National Key Lab of Computational Mathematics and Experimental Physics, Nandahongmen St. 1, Beijing 100076, China.

2. Professor, College of Aerospace Engineering, Nanjing Univ. of Aeronautics and Astronautics, Yudao St. 29, Nanjing, Jiangsu 210016, China.

3. Associate Professor, National Key Laboratory of Transient Physics, Nanjing Univ. of Science and Technology, Xiaolingwei St. 200, Xuanwu District, Nanjing, Jiangsu 210000, China (corresponding author).

4. Ph.D. Student, College of Aerospace Engineering, Nanjing Univ. of Aeronautics and Astronautics, Yudao St. 29, Nanjing, Jiangsu 210016, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

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

Reference25 articles.

1. Abbeel, P., A. Coates, M. Quigley, and A. Ng. 2006. “An application of reinforcement learning to aerobatic helicopter flight.” In Advances in Neural Information Processing Systems 19: Proc. of the 2006 Conf., 19. New York: IEEE.

2. Deep Reinforcement Learning: A Brief Survey

3. Suppression of Wing Rock in Slender Delta Wing by Horizontal Strakes

4. Clarke S. G. and I. Hwang. 2020. “Deep reinforcement learning control for aerobatic maneuvering of agile fixed-wing aircraft.” In Proc. AIAA Scitech 2020 Forum 2020. Reston VA: American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2020-0136.

5. Fujimoto S. H. Hoof and D. Meger. 2018. “Addressing function approximation error in actor-critic methods.” In Proc. Int. Conf. on Machine Learning 1587–1596. Stockholm Sweden: ICML.

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