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
Hauptverfasser: Liu, Xiaodong, Jia, Chenhui, Yang, Qingjun, Wu, Tiancai, Zhang, Yu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 17.11.2023
Schlagworte:
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)
ISSN:2688-0938
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Zusammenfassung: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.
ISSN:2688-0938
DOI:10.1109/CAC59555.2023.10450877