The Benchmark Active Controls Technology model aerodynamic data

Scott, Robert C.; Hoadley, Sherwood T.; Wieseman, Carol D.; Durham, Michael H.

doi:10.2514/6.1997-829

Cited by 26 publications

(15 citation statements)

References 3 publications

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“…A single trailing-edge control surface is used to control the air flow, thereby providing more maneuverability to suppress instability. This model is accurate for airfoils at low velocity and has been confirmed by wind tunnel experiments [16][17][18].…”

Section: Problem Of Stabilizing the Nonlinear Aeroelastic Systemmentioning

confidence: 51%

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Quadratic stabilization of a nonlinear aeroelastic system using a novel Neural-Network-based controller

Zhang

Söffker

2011

Sci. China Technol. Sci.

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This contribution proposes a novel neural-network-based control approach to stabilize a nonlinear aeroelastic wing section. With the prerequisite that all the states of the system are available, the proposed controller requires no comprehensive information about structural nonlinearity of the wing section. Furthermore, the proposed control approach requires no human intervention of designing goal dynamics and formulating control input function, which is difficult to be realized by the typical neural-network-based control following an inverse control scheme. Simulation results show that the proposed controller can stabilize the aeroelastic system with different nonlinearities. quadratic stabilization, neural-network, nonlinear aeroelastic control, cognition Citation:Zhang F, Söffker D. Quadratic stabilization of a nonlinear aeroelastic system using a novel Neural-Network-based controller.

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Section: Problem Of Stabilizing the Nonlinear Aeroelastic Systemmentioning

confidence: 51%

“…Spurred by this motivation, a new kind of NN-based controller is proposed in this paper to stabilize an aeroelastic system developed from the research of the Benchmark Active Control Technology (BACT) wind-tunnel model designed at the NASA Langley Research Center [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Quadratic stabilization of a nonlinear aeroelastic system using a novel Neural-Network-based controller

Zhang

Söffker

2011

Sci. China Technol. Sci.

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“…Secondly, the suction and pressure side of the existing profile could be replaced by the active surface as done by Lindroos et al (Lindroos et al, 2008). Finally, the surface could be mounted near the trailing edge as a spoiler (Scott et al, 1997). This has a similar effect as microtabs (Standish andvan Dam, 2005, Mayda et al, 2005), which are already under investigation for load control on wind …”

Section: Implementation On Wind Turbinesmentioning

confidence: 91%

A high-rate shape memory alloy actuator for aerodynamic load control on wind turbines

Lara-Quintanilla

Hulskamp

Bersee

2013

Journal of Intelligent Material Systems and Structures

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This paper discusses the development of a high rate shape memory alloy (SMA) driven actuator. The concept of the actuator was developed to act as aerodynamic load control surface on wind turbines. It was designed as a plate or beam-like structure with prestrained SMA wires embedded off its neutral axis. Moreover, the SMA material was embedded in channels through which air was forced to actively cool the wires when the recovery load was to be released. Wires were implemented on both sides of the neutral axis to deflect the beam in both directions. Thermal analysis of the cooling channels showed that they increased the cooling up to tenfold in comparison to the same set-up without forced convection. Subsequently a fuzzy logic controller was designed to control the thermo-mechanical system. The inputs were the error between the deflection and the set point, the value of the set point and the time derivative of the set point. The output consisted of two signals to the valves that controlled the flow through the channels and a signal heating signal that was split to both sets of wires, depending on its sign. The controller was tested on an antagonistic set-up, through which a similar thermo-mechanic behaviour as with the actuator was obtained, but eliminating the beam dynamics. The results were satisfactory; an actuation bandwidth of 1Hz was attained. Subsequently, the controller was tested on the actuator. With increasing actuation frequency, until 0.6Hz a relatively small error between the set point and the actual deflection was observed. Above that frequency the error increased, but also the sinusoidal response was lost. This is believed to be due to snap-through behaviour around the neutral position of the actuator. This was substantiated by the apparent inability of the actuator to track the set point around the neutral position in tracking a composite sinusoidal set point.

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“…Transonic flutter occurs at approximately Mach number 0.77. One flutter mechanism causes classical flutter instability [5]. This model has provided a testbed for the development and testing of passivity based robust control, LPV gain scheduling control, H-infinity, p-synthesis generalized predictive control, fuU order and reduced order LQG (Linear Quadratic Gaussian), and others.…”

Section: ~Ntroducllonmentioning

confidence: 99%

Fuzzy gain scheduling using output feedback for flutter suppression in unmanned aerial vehicles with Piezoelectric materials

Applebaum

Ben-Asher

2004

IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.

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This research addresses the modeling, analysis, and control of flutter, in a high order nonminimum phase aeroservoelastic UAV system with Piezoelectric single servo input Flutter suppression requires output feedback supplied by four accelerometer sensor readings. Linear time invariant plant models vary with the exogenous parameter, velocity. The f w gain scheduling control strategy, io conjunction with Linear Quadratic Gaussian regulation, with prescribed degree of stability, at nominal operating conditions, produces interpolated controller and observer gains over a uniform grid of operating velocities. "Wont case" highest velocity eigenvalues are used for stabilization. Simulations of augmented closed-loop systems show settling times ranging between 0.2 s and 0.4 s. Control efTort is uniform and converges to 0 rad within 0.1 s. The end result is a robust global controller that suppresses flutter over the entire velocity envelope. I. ~NTRoDUCllONOver the past several years, flutter suppression techniques have been actively investigated, on the Benchmark Active Control Technology (BACT) wind-tunnel model, developed by NASA Langley Research Center [1,2,3,4]. The BACT statespace design model consists of ten states and is parameterized by dynamic pressure. Transonic flutter occurs at approximately Mach number 0.77. One flutter mechanism causes classical flutter instability [5]. This model has provided a testbed for the development and testing of passivity based robust control, LPV gain scheduling control, H-infinity, p-synthesis generalized predictive control, fuU order and reduced order LQG (Linear Quadratic Gaussian), and others.Recently, altemative methods, for achieving flutter suppression, in high-order nonminimum phase systems, with three flutter mechanisms, have been proposed. These control strategies include 1) gain scheduling techniques for interpolating between linear time invariant (LTI), parameter dependent controllers [6,3,7], 2) the construction of a single LQG regulator satisfying a prescribed degree of stability QDS) criterion[S], and 3) recurrent neural networks for adaptive predictive control [9].In the case of gain scheduling, LMI (Linear Matrix Inequalities) techniques have generally been applied for gain construction. For the Piezoelectric system under study, feasibility gains could not be constructed, using LMI, for both the controller and observer modules. This was due, in large part, to the ill-conditioning properties of high-order state space matrices. In the case of the single LQG-PDS controller, robust performance could not be achieved for velocities below a certain value. The thiid alternative, recurrent neural networks, require extensive training times and extensive line-tuning of parameters for configuration of appropriate network topologies. Such networks achieve l i i t e d robustness over the flutter envelope.The current study introduces fuzzy gain scheduling as a technique for robust flutter suppression in a high-order n o n " phase UAV system that operates at various speeds at const...

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The Benchmark Active Controls Technology model aerodynamic data

Cited by 26 publications

References 3 publications

Quadratic stabilization of a nonlinear aeroelastic system using a novel Neural-Network-based controller

Quadratic stabilization of a nonlinear aeroelastic system using a novel Neural-Network-based controller

A high-rate shape memory alloy actuator for aerodynamic load control on wind turbines

Fuzzy gain scheduling using output feedback for flutter suppression in unmanned aerial vehicles with Piezoelectric materials

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