Mechanisms of active control in cylindrical fuselage structures

Silcox, R. J.; Fuller, Chris R.; Lester, H. C.

doi:10.2514/3.25231

Cited by 30 publications

(8 citation statements)

References 7 publications

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“…Control in physical coordinates is ideally suited for local control at the location of the error sensors used to construct the cost function. However, if global control of the reverberant system is desired in physical coordinates, then the actuators and sensors used to modify the system response must incorporate both spatial and temporal signal processing dependent upon the desired application as indicated in a number of references (Clark et al, 1992;Clark and Fuller, 1992c;Gibbs and Fuller, 1992b;Silcox et al, 1990;Sommerfeldt and Scott, 1993). In contrast, adaptive feedforward modal space control is ideal for global control of the reverberant system since each structural mode is controlled independently.…”

Section: Modal Coordinates Versus Physical Coordinatesmentioning

confidence: 96%

Adaptive feedforward modal space control

Clark

1995

The Journal of the Acoustical Society of America

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An alternative method of implementing adaptive feedforward control is presented in this work based upon independent modal space control of reverberant systems. Multivariable control theory is used to derive the optimal feedforward compensators, and when the error sensors are constructed in modal coordinates, the optimal compensators reduce to a single coefficient filter per control channel (assuming the reference signal is available). If the control inputs are also implemented in modal coordinates, then the control system is termed noninteracting since each mode is orthogonal to the other. Adaptive feedforward modal space control is ideal for implementation of time-averaged gradient descent algorithms such as Newton's method since the optimal filter coefficient is obtained from a single iteration of the algorithm, and is insensitive to changes in the resonant frequencies of the reverberant system. Formulating the control system in modal coordinates also clearly illustrates the effect of causality and nonminimum phase characteristics on the ultimate performance of any adaptive feedforward control system, whether it is implemented in physical coordinates or modal coordinates. ¸

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Section: Modal Coordinates Versus Physical Coordinatesmentioning

confidence: 96%

Adaptive feedforward modal space control

Clark

1995

The Journal of the Acoustical Society of America

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“…Effective noise control strategies can either reduce the vibration of the cylinder or magnify the vibration of the cylinder by shifting vibrational energy to shell modes that do not couple efficiently with acoustic modes. 8 This insight is important because aircraft manufacturers may reject a noise control method that increases vibration and, in turn, increases the potential for fatigue failure of the airframe.…”

Section: Problem Statementmentioning

confidence: 99%

“…3 However, methods for choosing good locations for the microphones and the actuators are needed. 8 In accordance with the notation used in reference 9, the ASAC optimization problem is to minimize the sum of squared pressures at a set of N f interior microphones:…”

Section: Problem Statementmentioning

confidence: 99%

Optimal sensor/actuator locations for active structural acoustic control

Padula

Palumbo

Kincaid

1998

39th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference and Exhibit

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Researchers at NASA Langley Research Center have extensive experience using active structural acoustic control (ASAC) for aircraft interior noise reduction. One aspect of ASAC involves the selection of optimum locations for microphone sensors and force actuators. This paper explains the importance of sensor/actuator selection, reviews optimization techniques, and summarizes experimental and numerical results.Three combinatorial optimization problems are described. Two involve the determination of the number and position of piezoelectric actuators, and the other involves the determination of the number and location of the sensors. For each case, a solution method is suggested, and typical results are examined. The first case, a simplified problem with simulated data, is used to illustrate the method. The second and third cases are more representative of the potential of the method and use measured data. The three case studies and laboratory test results establish the usefulness of the numerical methods.

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“…The control cost function is written in principal coordinates as (5) where a positive definite is included to penalize or limit control effort. Using the unitary property of the singular vector matrices and , this cost is readily rewritten in the more familiar form in physical coordinates as (6) where…”

Section: Theorymentioning

confidence: 99%

“…Early studies of this problem include the active control of sound fields inside simple cylindrical structures [1]- [3], and later more complex structures [4], [5]. In addition to loudspeakers as control actuators, structural inputs have been used to control interior noise, and dubbed active structural acoustic control (ASAC) [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

A principal component feedforward algorithm for active noise control: flight test results

Cabell

Palumbo

Vipperman

2001

IEEE Trans. Contr. Syst. Technol.

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An in-flight evaluation of a principal component algorithm for feedforward active noise control is discussed. Cabin noise at the first three harmonics of the blade passage frequency (103 Hz) of a Raytheon-Beech 1900D twin turboprop aircraft was controlled using 21 pairs of inertial force actuators bolted to the ring frames of the aircraft; 32 microphones provided error feedback inside the aircraft cabin. In a single frequency noise control test, the blade passage frequency was reduced by 15 dB averaged across the microphone array. When controlling the first three harmonics simultaneously, reductions of 11 dB at 103 Hz, 1.5 dB at 206 Hz, and 2.8 dB at 309 Hz were obtained. For single frequency feedforward control problems, the principal component algorithm is shown to be useful for reducing the computational burden and simplifying the implementation of control effort penalties in high channel count control systems. Good agreement was found between the in-flight behavior of the controller and the predicted optimal control solution.

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Mechanisms of active control in cylindrical fuselage structures

Cited by 30 publications

References 7 publications

Adaptive feedforward modal space control

Adaptive feedforward modal space control

Optimal sensor/actuator locations for active structural acoustic control

A principal component feedforward algorithm for active noise control: flight test results

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