Intelligent Flight Control for Cruise Vehicle Based on Backstepping Design and Reinforcement Learning
Aiming at velocity/height tracking control and attitude stabilization for the longitudinal flight of cruise vehicle, the research of intelligent flight control method is carried out by combining the backstepping control theory and the deep reinforcement learning method. Firstly, the longitudinal fli...
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| Veröffentlicht in: | Chinese Automation Congress (Online) S. 1247 - 1251 |
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| Format: | Tagungsbericht |
| Sprache: | Englisch |
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IEEE
17.11.2023
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| ISSN: | 2688-0938 |
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| Abstract | Aiming at velocity/height tracking control and attitude stabilization for the longitudinal flight of cruise vehicle, the research of intelligent flight control method is carried out by combining the backstepping control theory and the deep reinforcement learning method. Firstly, the longitudinal flight control system of the vehicle is decomposed into velocity and height subsystems, and the control law is designed based on backstepping control theory. Then, aiming at the problems that the selection of the multiple control gains will directly affect the control performance of the closed-loop system and the adjustment process of control gains is cumbersome, the twin delayed deep deterministic policy gradient algorithm (TD3) will be adopted to train the cruise vehicle, so that it can determine the control gains online according to the current flight states. The numerical simulation results show that, the proposed intelligent flight control method can guarantee the high-precision tracking with respect to height and velocity commands, and also realize the attitude stability of cruise vehicle. |
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| AbstractList | Aiming at velocity/height tracking control and attitude stabilization for the longitudinal flight of cruise vehicle, the research of intelligent flight control method is carried out by combining the backstepping control theory and the deep reinforcement learning method. Firstly, the longitudinal flight control system of the vehicle is decomposed into velocity and height subsystems, and the control law is designed based on backstepping control theory. Then, aiming at the problems that the selection of the multiple control gains will directly affect the control performance of the closed-loop system and the adjustment process of control gains is cumbersome, the twin delayed deep deterministic policy gradient algorithm (TD3) will be adopted to train the cruise vehicle, so that it can determine the control gains online according to the current flight states. The numerical simulation results show that, the proposed intelligent flight control method can guarantee the high-precision tracking with respect to height and velocity commands, and also realize the attitude stability of cruise vehicle. |
| Author | Jia, Chenhui Liu, Xiaodong Wu, Tiancai Yang, Qingjun Zhang, Yu |
| Author_xml | – sequence: 1 givenname: Xiaodong surname: Liu fullname: Liu, Xiaodong email: k.start@163.com organization: Beijing Aerospace Automatic Control Institute,National Key Laboratory of Science and Technology on Aerospace Intelligence Control,Beijing,China – sequence: 2 givenname: Chenhui surname: Jia fullname: Jia, Chenhui organization: Beijing Aerospace Automatic Control Institute,National Key Laboratory of Science and Technology on Aerospace Intelligence Control,Beijing,China – sequence: 3 givenname: Qingjun surname: Yang fullname: Yang, Qingjun organization: Beijing Aerospace Automatic Control Institute,Beijing,China – sequence: 4 givenname: Tiancai surname: Wu fullname: Wu, Tiancai organization: School of Automation Science and Electrical Engineering, Beihang University,Beijing,China – sequence: 5 givenname: Yu surname: Zhang fullname: Zhang, Yu organization: Beijing Aerospace Automatic Control Institute,National Key Laboratory of Science and Technology on Aerospace Intelligence Control,Beijing,China |
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| Snippet | Aiming at velocity/height tracking control and attitude stabilization for the longitudinal flight of cruise vehicle, the research of intelligent flight control... |
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| SubjectTerms | adaptive control Attitude control Backstepping backstepping control cruise vehicle Deep reinforcement learning Numerical simulation Process control Simulation Training twin delayed deep deterministic policy gradient algorithm (TD3) |
| Title | Intelligent Flight Control for Cruise Vehicle Based on Backstepping Design and Reinforcement Learning |
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