Optimal sensors placement and spillover suppression

Haniš, Tomáš; Hromčík, Martin

doi:10.1016/j.ymssp.2011.12.007

Cited by 21 publications

(9 citation statements)

References 12 publications

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“…It has been shown that residual modes tend to corrupt the sensor signal, leading to observation spillover. 16 Thus, optimization procedures, such as the modified EI procedure, 58 have been developed to place sensors on a BWB-type aircraft while minimizing residual mode information. The EI procedure is also sensitive to structure with many nodes, because nodes tend to be very close together and many good locations may fall in the same spot.…”

Section: Figure 6 Accelerometer Placement On Wing Modelmentioning

confidence: 99%

Modal Filtering for Control of Flexible Aircraft

Suh

Mavris

2013

54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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Modal regulators and deformation trackers are designed for an open-loop fluttering wing model. The regulators are designed with modal coordinate and accelerometer inputs respectively. The modal coordinates are estimated with simulated fiber optics. The robust stability of the closed-loop systems is compared in a structured singular-value vector analysis. Performance is evaluated and compared in a gust alleviation and flutter suppression simulation. For the same wing and flight condition two wing-shape-tracking control architectures are presented, which achieve deformation control at any point on the wing. Nomenclature= state matrix Accel = accelerometer AFS = active flutter suppression = balanced and reduced-order plant state matrix = number of accelerometers = control input matrix BWB = blended wing body = balanced and reduced-order input matrix = disturbance input weighting matrix for balanced and reduced-order plant = control input matrix for balanced and reduced-order plant = balanced = state space output matrix = balanced and reduced-order plant output matrix = output matrix transformation for accelerometers = state-regulated goal matrix for balanced and reduced-order plant = output matrix for balanced and reduced-order plant = number of control surfaces ̅ = mean aerodynamic chord = direct feedthrough matrix DFRC = Dryden Flight Research Center = control-regulated goal matrix for balanced and reduced-order plant = output noise weighting matrix for balanced and reduced-order plant = vector of deformations at fiber optic measurement stations = vector of virtual measurement deformations = vector of reference deformations at virtual measurement locations 2 ( ) = vector of deflections and rotations defined over at time ̈( ) = vector of accelerations and angular accelerations defined over at time EI = Effective Independence EOM = equations of motion = number of goal-regulated states and control surface movements = lower linear fractional transformation = plant GLA = gust load alleviation ( ) = class of all generalized plants with multiplicative input uncertainty ( ) = class of all generalized plants with multiplicative output uncertainty = gust model ( ) = matrix transfer function from disturbance to regulated output over all controllers ( ) = matrix transfer function from disturbance to regulated output = structural damping coefficient = sensor projection matrix (also referred to as hat matrix) = h infinity control type = scalar elements of the column and row of = identity matrix ( ) = imaginary part ̂ = imaginary number = matrix defined as = controller = control gain matrix = filter gain matrix = reduced frequency or non-dimensional frequency = system stability margin = global stiffness matrix ̃ = modal stiffness matrix = elemental stiffness matrix = leading edge = reference leading edge deflection at wing tip LFT = linear fractional transformation LQG = linear quadratic Gaussian LSE = least-squares estimation LTI = linear time-invariant = row vector index = row vector index for virtual deformation references = ...

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Section: Figure 6 Accelerometer Placement On Wing Modelmentioning

confidence: 99%

Modal Filtering for Control of Flexible Aircraft

Suh

Mavris

2013

54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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“…It has been shown that residual modes tend to corrupt the sensor signal, leading to observation spillover [25]. Thus, optimization procedures, such as the modified EI procedure [177], have been developed to place sensors on a BWB-type aircraft while minimizing residual mode information.…”

Section: Wing Rootmentioning

confidence: 99%

Robust Modal Filtering for Control of Flexible Aircraft

Suh

2013

AIAA Atmospheric Flight Mechanics (AFM) Conference

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me invaluable advice and contributed to my work. My foremost and sincere appreciation goes to Dr. Dimitri Mavris, my thesis advisor for his course corrections and thoughtful input which shaped my dissertation. Special thanks to Mr. Steve Jacobson, Branch Chief of NASA's Dryden Flight Research Center (DFRC), who inspired the thesis concept. Gratitude is owed to Mr. Martin Brenner for confirming that the modal filtering approach was mathematically sound from the beginning. I would like to acknowledge thesis committee members Dr.

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“…Alternatively for existing prototypes, the experimental identification approach can be applied to get the mathematical models directly via experimental modal analysis [3]. Model order reduction [1,4] then gives accurate enough yet tractable models for optimal actuators and sensors placement [5][6][7]. Finally, a very limited number of them are considered (say up to twenty) for the design of the control laws [1,8].…”

Section: Introductionmentioning

confidence: 99%

Low‐Complexity Decentralized Active Damping of One‐Dimensional Structures

Hušek

Svoboda

Hromčík

et al. 2018

Shock and Vibration

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In the paper, we propose distributed feedback control laws for active damping of one-dimensional mechanical structures equipped with dense arrays of force actuators and position and velocity sensors. We consider proportional position and velocity feedback from the neighboring nodes with symmetric gains. Achievable control performance with respect to stability margin and damping ratio is discussed. Compared to full-featured complex controllers obtained by modern design methods like LQG, H-infinity, or mu-synthesis, these simplistic controllers are more suitable for experimental fine tuning and are less case-dependent, and they shall be easier to implement on the target future smart-material platforms.

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Optimal sensors placement and spillover suppression

Cited by 21 publications

References 12 publications

Modal Filtering for Control of Flexible Aircraft

Modal Filtering for Control of Flexible Aircraft

Robust Modal Filtering for Control of Flexible Aircraft

Low‐Complexity Decentralized Active Damping of One‐Dimensional Structures

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