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Simultaneous optimization of the sensor and actuator positions for an active noise and/or vibration control system using genetic algorithms, applied in a Dornier aircraft

Simultaneous optimization of the sensor and actuator positions for an active noise and/or... Active noise control (ANC) became in the last decade a very popular technique for controlling low‐frequency noise. The increase in its popularity was a consequence of the rapid development in the fields of computers in general, and more specifically in digital signal processing boards. ANC systems are application specific and therefore they should be optimally designed for each application. Even though the physical background of the ANC systems is well‐known and understood, tools for the optimization of the sensor and actuator configurations of the ANC system based on classical optimization methods do not perform as required. This is due to the nature of the problem that allows the calculation of the effect of the ANC system only when the sensor and actuator configurations are specified. An additional difficulty in this problem is that the sensor and the actuator configurations cannot be optimized independently, since the effect of the ANC system is directly involved in the combined sensor and actuator configuration. For the solution of this problem several intelligent techniques were applied. In this paper the successful application of a genetic algorithm, an optimization technique that belongs to the broad class of evolutionary algorithms, is presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering Computations Emerald Publishing

Simultaneous optimization of the sensor and actuator positions for an active noise and/or vibration control system using genetic algorithms, applied in a Dornier aircraft

D.A. Manolas; I. Borchers; D.T. Tsahalis
Engineering Computations , Volume 17 (5): 11 – Aug 1, 2000
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Publisher
Emerald Publishing
Copyright
Copyright © 2000 MCB UP Ltd. All rights reserved.
ISSN
0264-4401
DOI
10.1108/02644400010339806
Publisher site
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Abstract

Active noise control (ANC) became in the last decade a very popular technique for controlling low‐frequency noise. The increase in its popularity was a consequence of the rapid development in the fields of computers in general, and more specifically in digital signal processing boards. ANC systems are application specific and therefore they should be optimally designed for each application. Even though the physical background of the ANC systems is well‐known and understood, tools for the optimization of the sensor and actuator configurations of the ANC system based on classical optimization methods do not perform as required. This is due to the nature of the problem that allows the calculation of the effect of the ANC system only when the sensor and actuator configurations are specified. An additional difficulty in this problem is that the sensor and the actuator configurations cannot be optimized independently, since the effect of the ANC system is directly involved in the combined sensor and actuator configuration. For the solution of this problem several intelligent techniques were applied. In this paper the successful application of a genetic algorithm, an optimization technique that belongs to the broad class of evolutionary algorithms, is presented.

Journal

Engineering Computations – Emerald Publishing

Published: Aug 1, 2000

Keywords: Optimization; Noise levels; Control; Genetic algorithms; Aircraft

References

  • Genetic Algorithms in Search, Optimization, and Machine Learning
    Goldberg, D.E
  • A genetic algorithm for active noise control actuator positioning
    Katsikas, S.K; Tsahalis, D.T; Manolas, D.A; Xanthakis S
  • Active Control of Sound
    Nelson, P. A; Elliott, S.J
  • A genetic algorithm for optimal positioning of actuators in active noise control: results from the ASANCA project
    Tsahalis, D.T; Katsikas, S.K; Manolas, D.A
  • Operation optimization of an industrial co‐generation system by a genetic algorithm
    Tsahalis, D.T; Frangopoulos, C.A; Manolas, D.A; Gialamas, T.P
  • A genetic algorithm for operation optimization of an industrial co‐generation system
    Tsahalis, D.T; Manolas, D.A; Gialamas, T.P; Frangopoulos C.A