Modal Filtering for Control of Flexible Aircraft

Suh, Peter M.; Mavris, Dimitri N.

doi:10.2514/6.2013-1741

Cited by 9 publications

(6 citation statements)

References 49 publications

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“…The primary goals of the X-56A efforts were to investigate active flutter suppression technology and active shape control. To date, several topics of research have been published and include: the stability of closed-loop flutter wing models [8,9]; stabilizing and controlling wing shape [10]; fly-by-feel sensing and control [11]; airframe structural optimization [12,13]; and most recently, CFD-based dynamic aeroelastic analysis for flutter prediction [14]. All of these research topics rely on a structural model of the aircraft that has been verified/validated with ground vibration test results [15].…”

Section: Geometrymentioning

confidence: 99%

Aeroelastic Indicial Response Reduced-Order Modeling for Flexible Flight Vehicles

Hiller

Frink

Silva

et al. 2020

Journal of Aircraft

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Section: Geometrymentioning

confidence: 99%

Aeroelastic Indicial Response Reduced-Order Modeling for Flexible Flight Vehicles

Hiller

Frink

Silva

et al. 2020

Journal of Aircraft

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“…These points were selected to maximize modal information for the first symmetric bending and torsion modes. 3 The virtual deformation controller tracks modally transformed references, thereby indirectly tracking deformations at these points. 2 …”

Section: Fiber Optic Sensor Placementmentioning

confidence: 99%

“…3 It is assumed that the subset of modes captures the main dynamics and the sensors are subject to random errors. This error can be modeled as a normal distribution ( ).…”

Section: A Strain M-estimator Derivationmentioning

confidence: 99%

“…2,3 In one study, the SFOS was laid out and simulated on the left wing and the right wing of the X-56A model. The sensors represent six fibers.…”

Section: Fiber Optic Sensor Placementmentioning

confidence: 99%

“…The FOS measure high-density strain measurements along the entire wing span. It has been shown that simulated fiber optic sensors (SFOS) enable both AFS and active shape control (ASC) for a simulated wing model 3 and the X-56A model. 2 At a certain flight speed, the X-56A models are subject to flutter in the flight envelope.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

Suh

Chin

Mavris

2014

AIAA Atmospheric Flight Mechanics Conference

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The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable. Nomenclature= maximum desired strain variation on sensors upstream of the fiber break = bias on sensor near the fiber break = current M-step = number of M-steps = current concentration step = number of concentration steps = squared Mahalanobis distance ( ) = squared Mahalanobis distance of the argument = upper bound of ( ) = deformations defined at time = reference deformations = finite residuals of all sensors = finite residual of the sensor = plant = tuning constant for weight function of M-estimator = controller = sensor station 1 Aerospace Engineer, PhD; NASA Armstrong, Controls Branch / 4840D; AIAA Member; peter.m.suh@nasa.gov. 2 Aerospace Engineer; NASA Armstrong, Aerostructures Branch / 4820; alexander.w.chin@nasa.gov. 2 = index of SFOS used for sensor feedback = row index vector of for reference deformations ( ) = median of the argument = number of mode shapes retained in the model = column index vector of for reference modal displacements = number of nodes in finite element model ( ) = normal distribution with argument parameters = airframe sensor noise = bias induced by simulated FOS failure on sensors = simulated fiber optic sensor noise = tuning constant ( ) = position of fiber break ( ) = positions of sensors in a radius upstream of the fiber break ( ) = vector of modal displacements at time ( ) = modal displacement at time ̂ = estimated modal displacements ̂( ) = estimated modal displacements at time step ( ) = modal displacement references = radius of sensors affected near the fiber break = set of all sensors = set of good sensors in concentration step = set of sensors upstream and near the fiber break ̂ ( ) = SFOS strain measurements ( ) = measured strain of sensor after the fiber break (...

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Optimal design of strain sensor placement for distributed static load determination

Morris,

Davis

2023

Inverse Problems

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In many applications it is desirable to inverse-calculate the distributed loading on a structure using a limited number of sensors. Yet, the calculated loads can be extremely sensitive to the placement of these sensors. In the case of predicting point loading applied at a known location, best results are typically achieved when one sensor is collocated with the force. However, the extension of this rule to distributed loading remains uncertain, and even simple sensor system design scenarios often require the designer to directly optimize the sensor placements using a numerical model. In an effort to provide designers with guidance, we identify optimal sensor configurations for predicting static distributed loads on beams with classical boundary conditions. An influence coefficient method, wherein the strain is related linearly to the static load, is used to estimate the applied forces. The loading distribution on the structure is assumed to be either a piece-wise linearly-distributed load or a uniformly-distributed load, allowing for distributed loads to be estimated using the magnitudes of a small number of control points. Given the simplicity of the beam structure, the equations of the influence coefficient method are derived analytically, which allows for the sensor placement to be specified using continuous optimization methods. The condition number of the influence coefficient matrix is used as a surrogate for error during optimization. ‘Rules of thumb’ for sensor placement are presented based on the optimization results. Results show that the optimal and rule-of-thumb sensor configurations are more resistant to input noise than naïve configurations, with the rule-of-thumb configurations yielding similar force predictions relative to the optimal configurations. We expect the rules of thumb to be useful guidelines for engineers designing tests on beam-like structures such as aircraft wings or marine propellers where the inverse calculation of distributed loads is of interest.

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Modal Filtering for Control of Flexible Aircraft

Cited by 9 publications

References 49 publications

Aeroelastic Indicial Response Reduced-Order Modeling for Flexible Flight Vehicles

Aeroelastic Indicial Response Reduced-Order Modeling for Flexible Flight Vehicles

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

Optimal design of strain sensor placement for distributed static load determination

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