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Asymmetric Airfoil Morphing via Deep Reinforcement Learning.
Biomimetics (Basel, Switzerland)
|November 22, 2022
Summary
This study introduces a new deep reinforcement learning method for airfoil morphing in aircraft wings. The novel approach enhances performance by an average of 29.8% compared to traditional techniques.
Area of Science:
- Aerospace Engineering
- Artificial Intelligence
Background:
- Morphing aircraft wings offer improved performance by adapting geometry to flight conditions.
- Controlling shape memory alloy actuation for morphing presents challenges due to hysteresis.
Purpose of the Study:
- To propose a novel deep reinforcement learning (DRL) control method for asymmetric airfoil morphing.
- To address and incorporate the hysteresis characteristics of shape memory alloys in the control strategy.
Main Methods:
- Developed an asymmetric airfoil using Bézier curves and shape memory alloys actuated by resistive heating.
- Formulated a reinforcement learning environment using a second-order Markov decision process to model hysteresis.
- Applied DRL techniques to learn a morphing policy without requiring accurate system model information.
Main Results:
- Simulations validated the effectiveness of the proposed DRL-based morphing control method.
- The novel approach demonstrated an average performance improvement of 29.8% over traditional methods.
Conclusions:
- The proposed DRL method successfully controls asymmetric airfoil morphing, considering shape memory alloy hysteresis.
- This approach offers significant performance gains for morphing aircraft wings.
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