Smart wing load alleviation through optical fiber sensing, load identification, and deep reinforcement learning

Abstract

Abstract 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 also applicable to another scenario involving variations in the target loads, and the results indicated the generalized applicability of the neural-network-based controller trained via deep reinforcement learning.