Bibliographic Details
| Title: |
Structural sensing of interior sound for active control of noise in structural-acoustic cavities. |
| Authors: |
Bagha, Ashok K.1, Modak, S. V.1 svmodak@mech.iitd.ac.in |
| Source: |
Journal of the Acoustical Society of America. Jul2015, Vol. 138 Issue 1, p11-21. 11p. |
| Subjects: |
Noise control research, Structural acoustics, Potential energy, Noise measurement, Acoustic filters |
| Abstract: |
This paper proposes a method for structural sensing of acoustic potential energy for active control of noise in a structural-acoustic cavity. The sensing strategy aims at global control and works with a fewer number of sensors. It is based on the established concept of radiation modes and hence does not add too many states to the order of the system. Acoustic potential energy is sensed using a combination of a Kalman filter and a frequency weighting filter with the structural response measurements as the inputs. The use of Kalman filter also makes the system robust against measurement noise. The formulation of the strategy is presented using finite element models of the system including that of sensors and actuators so that it can be easily applied to practical systems. The sensing strategy is numerically evaluated in the framework of Linear Quadratic Gaussian based feedback control of interior noise in a rectangular box cavity with a flexible plate with single and multiple pairs of piezoelectric sensor-actuator patches when broadband disturbances act on the plate. The performance is compared with an "acoustic filter" that models the complete transfer function from the structure to the acoustic domain. The sensing performance is also compared with a direct estimation strategy. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |