The use of optical fiber sensors has been considered to realize smart structures, which can sense and respond to environments. To develop this concept in aviation, this paper reports on a smart wing framework that senses and responds to the environment to alleviate the wing structural loads. The wing is equipped with optical fiber sensors that measure the strain distributions on the wing surface. Considering the strains, a group of neural networks determine the wing load distributions and angle of attacks. This information is fed into a controller that drives multiple flaps to re-distribute the loads. The controller is trained via a deep reinforcement learning technique. The wind tunnel experiments demonstrated that the proposed closed-loop control could alleviate the bending moment by 56.6% on average over the test duration from the initial state while the total load variations could be maintained within a range of ±5 N for 87.1% of the test duration. The proposed approach was ...

中文翻译：

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通过光纤传感，载荷识别和深度强化学习，智能地减轻机翼载荷

已经考虑使用光纤传感器来实现可以感知和响应环境的智能结构。为了在航空中发展这一概念，本文报告了一种智能机翼框架，该框架可以感知并响应环境，从而减轻机翼的结构负荷。机翼装有光纤传感器，可测量机翼表面的应变分布。考虑到应变，一组神经网络确定机翼载荷分布和迎角。该信息被输入到控制器中，该控制器驱动多个襟翼以重新分配载荷。通过深度强化学习技术来训练控制器。风洞实验表明，所提出的闭环控制可以将弯矩减小56。从初始状态开始的整个测试持续时间平均为6％，而总负载变化可以在87.1％的测试持续时间内保持在±5 N的范围内。拟议的方法是...