1. Adaptive control method for morphing trailing-edge wing based on deep supervision network and reinforcement learning.
- Author
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Dai, Jiahua, Liu, Peiqing, Kong, Chuihuan, Pan, Lijun, and Si, Jiangtao
- Subjects
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DEEP reinforcement learning , *REINFORCEMENT learning , *ADAPTIVE control systems , *DRAG reduction , *REAL-time control - Abstract
• A novel adaptive control method based on DSN and RL is proposed to solve a problem of controlling the morphing trailing edge for drag reduction. • Due to the construction of network based on the physical prior knowledge, DSN has better accuracy in predicting lift and pitching moment than the traditional surrogate models. • After training, the controller can solve multi-dimensional and nonlinear complex problems. Under the condition of maintaining pitching moment balance, the lift-to-drag ratio at the start point can be increased by 1.36%, and can be increased by 2.61% at the end point. The morphing trailing-edge wing can adaptively change its shape according to the different flight conditions, improving cruising efficiency. The control of the morphing trailing edge is a high-dimensional variable and nonlinear problem, facing the difficulty of the high training cost and the difficulty in real-time continuous control. To overcome these difficulties, a novel adaptive control method based on the couple of Deep Supervision Net (DSN) and Deep Reinforcement Learning (DRL) is proposed to solve a control problem of the morphing trailing-edge wing. DSN uses distributed loads to improve aerodynamic prediction accuracy compared to the traditional surrogate models, while DRL conducts real-time control and improves the performance at non-design points compared to the traditional multi-design points optimization. After control, the lift-to-drag ratio at the start point can be increased by 1.36%, and can be increased by 2.61% at the end point tested on a commercial wide-body aircraft model. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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