지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.11
- 수록면
- 1,022 - 1,027 (6page)
- DOI
- 10.5302/J.ICROS.2022.22.0143
이용수
초록· 키워드
오류제보하기
This paper presents a proximal policy optimization (PPO)-based model reference tracking controller design for quadrotor unmanned aerial vehicles (UAVs). First, the quadrotor UAV is divided into the attitude and position systems, and each system is expressed as a nonlinear state-space equation. Thereafter, we design a linear reference model, which is used to derive the tracking error dynamics. The proposed neural network model implements a PPO-based reinforcement learning algorithm consisting of an actor approximating a policy and a critic corresponding to a state-value function. The actor receives the state variables of the tracking error dynamics as input and outputs the thrust values to be applied to each axis. Further, we decentralize the PPO-based controller into the attitude and position controllers, which are then trained separately. For learning purposes, we implement an environment code that expresses the tracking error dynamics of the quadrotor by extending the OpenAI Gym environment. Finally, a simulation example is provided to show the position tracking performances of up to 0.0166 m and 0.0254 m for the horizontal and vertical axes, respectively.
목차
Abstract
I. Introduction
II. Preliminaries
III. Main Results
IV. Simulation Results
V. Conclusions
REFERENCES
참고문헌 (17)
참고문헌 신청
H. S. Kim, K. Lee, and Y. H. Joo, “Decentralized sampled-data fuzzy controller design for a VTOL UAV,” Journal of the Franklin Institute, vol. 358, no. 3, pp. 1888-1914, 2021.
S. Shao, M. Chen, J. Hou, and Q. Zhao, “Event-triggered-based discrete-time neural control for a quadrotor UAV using disturbance observer.” IEEE/ASME Transactions on Mechatronics, vol. 26, no. 2, pp. 689-699, 2021.
M. Sun, J. Liu, H. Wang, X. Nian, and H. Xioing, “Robust fuzzy tracking control of a quad-rotor unmanned aerial vehicle based on sector linearization and interval matrix approaches,” ISA Transactions, vol. 80, pp. 336-349, 2018.
J. W. Kim, B. J. Park, H. Yoo, T. H. Oh, J. H. Lee, and J. M. Lee, “A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system,” Journal of Process Control, vol. 87, pp. 166-178, 2020.
X. Ding, W. Du, and A. E. Cerpa, “MB2C: Model-based deep reinforcement learning for multi-zone building control,” Proceedings of the 7th ACM ICSEEBCT, pp. 50-590, 2020.
S. Shao, M. Chen, J. Hou, and Q. Zhao, “Event-triggered-based discrete-time neural control for a quadrotor UAV using disturbance observer.” IEEE/ASME Transactions on Mechatronics, vol. 26, no. 2, pp. 689-699, 2021.
M. Sun, J. Liu, H. Wang, X. Nian, and H. Xioing, “Robust fuzzy tracking control of a quad-rotor unmanned aerial vehicle based on sector linearization and interval matrix approaches,” ISA Transactions, vol. 80, pp. 336-349, 2018.
J. W. Kim, B. J. Park, H. Yoo, T. H. Oh, J. H. Lee, and J. M. Lee, “A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system,” Journal of Process Control, vol. 87, pp. 166-178, 2020.
X. Ding, W. Du, and A. E. Cerpa, “MB2C: Model-based deep reinforcement learning for multi-zone building control,” Proceedings of the 7th ACM ICSEEBCT, pp. 50-590, 2020.
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