Transonic flutter suppression control law design using classical and optimal techniques with wind tunnel results

Mukhopadhyay, Vivek

doi:10.2514/6.1999-1396

Cited by 6 publications

(8 citation statements)

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“…In order to validate the basic structural and the aerodynamic model presented in this paper, openloop simulation results have been compared with the BACT study data obtained by Mukhopadhyay at NASA Langley Research Center [4]. In terms of the flutter dynamic pressure and frequency at Mach number, the numerical model of the NASA study on BACT system gives the following results ⎧ ⎪ ⎨ ⎪ ⎩ P dyn = 6.228 kPa( ∼ =128 psf ) ω = 28.2 rad/s Mach = 0.5 in good agreement with the simulation data of the present model.…”

Section: Open-loop Simulationsmentioning

confidence: 99%

“…During the last decade, extensive work was devoted to active flutter suppression at NASA Langley Research Center. Both the Benchmark Active Control Technology (BACT) project [1][2][3][4][5][6] and the Active Flexible Wing project [7] are considered. The BACT and wind tunnel test programme was started with the objective of investigating non-linear, unsteady aerodynamics and active flutter suppression of wings in transonic flow.…”

Section: Introductionmentioning

confidence: 99%

“…A double-feedback cycle allows to reach superior stability robustness and performances with respect to other control laws designed using the classical control theory [4].…”

Section: Introductionmentioning

confidence: 99%

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Wing flutter suppression enhancement using a well-suited active control model

Marretta

Marino

2007

Proceedings of the Institution of Mechanical Engineers, Part G:

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State-space theory is employed here to model a new active wing flutter suppression control. In this paper, the design of a flutter suppression control law, for the NASA Benchmark Active Control Technology wing, is proposed through a single input-single output controller and unsteady aerodynamics is modelled using the Theodorsen's theory. Wing dynamic model is obtained by combining the aeroelastic equations of motion with the actuator model presented. Open-loop dynamic behaviour is examined for a single feedback variable that combines pitch and plunge accelerations. Here, a new formulation of control law, based on classical control techniques and featuring two feedback closed-loops, is successfully employed to reach superior stability robustness with respect to other control laws designed with classical flutter suppression schemes having only one feedback closed-loop. The process allows the stability of the aeroelastic system for flight speeds that exceed the open-loop flutter velocity.Several control strategies have been applied to suppress wing aeroelastic instabilities. During the last decade, extensive work was devoted to active flutter suppression at NASA Langley Research Center. Both the Benchmark Active Control Technology (BACT) project [1-6] and the Active Flexible Wing project [7] are considered. The BACT and wind tunnel test programme was started with the objective of investigating non-linear, unsteady aerodynamics and active flutter suppression of wings in transonic flow.Moreover, in detail, the BACT is a rigid rectangular scaled wing with an NACA 0012 aerofoil section (Fig. 1). It is equipped with a partial span trailing-edge control surface and with upper/lower spoilers, which are positioned by hydraulic actuators. A wind-tunnel model is instrumented with linear accelerometers that are located at each corner of the wing and they are used as the primary sensors for feedback control. The wing is mounted on a device called pitch and plunge apparatus (PAPA) that provides the two flexible degrees of freedom needed for classical flutter. The PAPA consists of a fixed plate, a set of four fixed-end rods, a rectangular shaped drag strut, and a moving plate linked with the BACT wing.Recently, many strategies and algorithms have been performed for the two main control methods: a JAERO98

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Section: Open-loop Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wing flutter suppression enhancement using a well-suited active control model

Marretta

Marino

2007

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Various flutter control architectures have been developed for AFS, such as the aerodynamic energy concept, 23 classical, 24 linear quadratic Gaussian (LQG), 7,25 , , 26 eigenspace, 27 minimax, 28 predictive, 29 neuro-adaptive, 2,30 and many others. Most of these designs are supported by a few point sensors for feedback.…”

Section: Introductionmentioning

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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“…The time-accurate interaction between structural dynamics, the flight control system, and aerodynamics required for this work (known as aeroservoelasticity), has recently received attention, and previous work by various authors has demonstrated the ability of heave and pitch feedback control acting on conventional control surfaces (e.g. ailerons and spoilers) to increase the flutter margin of both twoand three-dimensional aerofoils in the computational and real domains [12][13][14][15][16][17][18][19][20][21][22][23][24]. However, the gain selection has generally been achieved by standard optimization techniques acting upon a linearized version of the physical system.…”

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confidence: 99%

A comparison of full non‐linear and reduced order aerodynamic models in control law design using a two‐dimensional aerofoil model

Allen

Taylor

Fenwick

et al. 2005

Numerical Meth Engineering

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SUMMARYThe prediction of the flutter boundary of an aircraft is a necessary but time consuming process, particularly as for the most realistic results a time accurate simulation of the interaction between the non-linear aerodynamic and structural forces is required. Extension of the flight envelope by the design of active control laws to suppress flutter further increases the demands on computational time, to presently unrealistic levels. Use of a reduced order model (ROM) derived from, and in place of, the full non-linear aerodynamics greatly reduces the time required for calculation of aerodynamic forces. However, this is necessarily accompanied by some loss in accuracy, and hence the method must be verified by comparison with results obtained by the full aerodynamic model before it may be used with confidence.Such a comparison is presented here, using a two-dimensional aerofoil and control surface combination as a test case. Active control of the deflectable surface is used to attempt to increase the flutter speed across the complete Mach range, feedback control being achieved by gains acting on heave and pitch proportional and differential signals, interpreted as a hinge moment demand. Full non-linear and reduced order aerodynamic models are then used to obtain optimum control law gain for flutter suppression. The results demonstrate that the ROM accurately predicts the open loop flutter boundary, gives a good approximation to the increase in flutter speed that may be produced by gain optimization, and produces a similar response given the identical gain values in each system for a significantly reduced cost.

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Transonic flutter suppression control law design using classical and optimal techniques with wind tunnel results

Cited by 6 publications

References 11 publications

Wing flutter suppression enhancement using a well-suited active control model

Wing flutter suppression enhancement using a well-suited active control model

Modal Filtering for Control of Flexible Aircraft

A comparison of full non‐linear and reduced order aerodynamic models in control law design using a two‐dimensional aerofoil model

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