Fixed-Wing Stalled Maneuver Control Technology Based on Deep Reinforcement Learning

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Fixed-Wing Stalled Maneuver Control Technology Based on Deep Reinforcement Learning

Hu, Weijun / Gao, Zhiqiang / Quan, Jiale / Ma, Xianlong / Xiong, Jingyi / Zhang, Weijie

A fixed-wing flight control method based on Deep Reinforcement Learning (DRL) was proposed to solve the problem of strong coupling between the channels of a fixed-wing aircraft during actual flight control, and the strong non-linearity and uncertainty of the aerodynamic parameters of the aircraft during stalled maneuver. When designing the control system, Proximal Policy Optimization (PPO) and neural network are used to design a control method that is directly mapped from the state to the aircraft actuator. A formalized reward function is designed to simulate the control of the vehicle's stalled maneuver, taking into account the relationship between the position and attitude of the vehicle and the actual demand command. The simulation results show that the control system design based on PPO algorithm can reduce the dependence on the model, achieve the intelligent control of the aircraft.

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Dokumentinformationen

Titel :

Fixed-Wing Stalled Maneuver Control Technology Based on Deep Reinforcement Learning

Beteiligte:

Hu, Weijun (Autor:in) / Gao, Zhiqiang (Autor:in) / Quan, Jiale (Autor:in) / Ma, Xianlong (Autor:in) / Xiong, Jingyi (Autor:in) / Zhang, Weijie (Autor:in)

Erschienen in:

2022 IEEE 5th International Conference on Big Data and Artificial Intelligence (BDAI) ; 19-25

Erscheinungsdatum :

2022-07-08

Format / Umfang :

806566 byte

ISBN :

978-1-6654-7081-0 , 978-1-6654-7080-3

DOI :

https://doi.org/10.1109/BDAI56143.2022.9862674

Medientyp :

Aufsatz (Konferenz)