Adaptive Load Control of Flexible Aircraft Wings Using Fiber Optic Sensing

Peña, Francisco de la; Martins, Benjamin L.; Richards, W. Lance

doi:10.1364/ofs.2018.wf101

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Segmented ailerons improve the lift‐distribution profile of the wing and help in mitigating the drag 16 . Recent literature reports a project under the flag of NASA 18 where the wing‐load is actively distributed along the wing using multiple ailerons. The UAV uses optical sensors to pick up any anomalies in the wing‐lift distribution profile and actuate corresponding control segments to avoid wing fluttering.…”

Section: Introductionmentioning

confidence: 99%

Disturbance rejection enhancement using predictive control for the fixed‐wing UAV with multiple ailerons

Sattar

Wang

Ansari

et al. 2023

Adaptive Control & Signal

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Summary The performance of small fixed‐wing unmanned aerial vehicles (UAVs) is easily degraded by exogenous disturbances. In an attempt to improve the performance, the structural change is made in conventional small fixed‐wing UAV through segmentation of each aileron control surface into multiples. Detailed system identification experiments are performed on each aileron pair in the wind tunnel to acquire linear dynamic models for the roll attitude of the UAV. These experiments have provided the transfer functions for the aileron control surfaces based on corresponding frequency response. Three distinctive model predictive controllers are designed and deployed in real‐time hardware to achieve the roll attitude control. The roll attitude control experiments are validated in wind tunnel under both normal and turbulent environments. The results show that multiple input and single output control system provides significant improvement in the roll attitude and disturbance rejection performance. The collective actuation of multiple control surfaces improves roll stability by 10.1%$$ 10.1\% $$ to 67.87%$$ 67.87\% $$ when compared to the single aileron pair based conventional control in the presence of turbulent flight conditions.

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Section: Introductionmentioning

confidence: 99%

Disturbance rejection enhancement using predictive control for the fixed‐wing UAV with multiple ailerons

Sattar

Wang

Ansari

et al. 2023

Adaptive Control & Signal

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“…A reduction of the wing root bending moment is initiated by a shift of the local lift forces toward the wing root. Numerical and experimental studies used this concept to demonstrate its potential for both gust load alleviation [2,6,7] and maneuver load alleviation [8,9]. Besides application to operational aircraft, dynamic trailing edge flaps for maneuver load alleviation and gust load alleviation are investigated at low speed conditions both experimentally and numerically e.g.…”

Section: Introductionmentioning

confidence: 99%

Approach for Aerodynamic Gust Load Alleviation by Means of Spanwise-Segmented Flaps

Ullah¹,

Lutz²,

Klug³

et al. 2023

Journal of Aircraft

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Active gust load alleviation techniques exhibit a high potential in significantly reducing the transient gust loads on aircraft. In this work the aerodynamic potential of trailing-edge flaps and leading-edge flaps is numerically studied with the purpose to significantly reduce the structural gust loads. The utilized spanwise-segmented flaps represent slight modifications of existing devices for high-lift and maneuvering. The investigations based on unsteady Reynolds-averaged Navier–Stokes computations are conducted by employing a generic wing–fuselage aircraft configuration at transonic flow conditions. Idealized discrete “[Formula: see text]”-type vertical gusts that are relevant for the certification process are used as representative atmospheric disturbances. The focus of this paper is to introduce a practicable prediction method for required trailing- and leading-edge flap deflections for a significant mitigation of gust-induced wing loads. The three-dimensional flap deflections are determined by parametric two-dimensional simulations at representative wing sections. Different extensions of the estimation approach are investigated to assess the influence of the wing planform, the finite wing span, the aerodynamic phase lags, and the flap scheduling. It is shown that the trailing- and leading-edge flaps are promising in terms of alleviation of gust-induced wing bending and wing torsional moments, respectively. However, at high leading-edge flap deflections that are necessary for a full compensation of the wing torsional moment large-scale flow separation is identified. The introduced gust load alleviation approach indicates a good transferability between two-dimensional airfoil and three-dimensional wing aerodynamics for unsteady flap deflections.

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“…This type of active control of the aerodynamic performance can also lead to load alleviation and flutter suppression. It has been noted that the active control of multiple flaps enables a redistribution of the aerodynamic load, and thus, the structural loads on the wings reduce over the complete mission profile [12]. In this regard, optical fiber sensors have been used to determine the wing deflections, based on which, a proportional controller was used to actuate the flaps.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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.

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Adaptive Load Control of Flexible Aircraft Wings Using Fiber Optic Sensing

Cited by 3 publications

References 2 publications

Disturbance rejection enhancement using predictive control for the fixed‐wing UAV with multiple ailerons

Disturbance rejection enhancement using predictive control for the fixed‐wing UAV with multiple ailerons

Approach for Aerodynamic Gust Load Alleviation by Means of Spanwise-Segmented Flaps

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

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