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A Novel Multiple-Reference Algorithm for Active Control of Propeller-Induced Noise in Aircraft Cabins
Responsible organisation
2000 (English)Report (Other academic)
Abstract [en]

The cabin noise inside propeller aircraft is essentially dominated by strong tonal components at harmonics of the blade passage frequency of the propellers. In order to achieve an efficient reduction of such a periodic low frequency noise, it is advisable to use an active noise control system based on adaptive narrowband feedforward techniques. The feedforward controller presented in this paper exploits narrowband assumptions by using complex-valued filtering and complex modeling of control paths. This paper introduces a multiple reference controller based on the novel actuator-individual normalized Filtered-X Least-Mean-Squares (FX LMS) algorithm. This algorithm combines low computational complexity with high performance. The algorithm is of the LMS-type. However, owing to the novel normalization of the algorithm it can also be regarded as a Newton-type algorithm. A comparison between the actuator-individual normalized FX LMS algorithm and the ordinary normalized FX LMS algorithm is presented. The results demonstrate better performance in terms of convergence rate and tracking properties when the Newton-like actuator-individual normalized FX LMS algorithm is used as compared with the conventional normalized LMS algorithm. The evaluation was performed using noise signals recorded inside the cabin of a twin engine propeller aircraft during flight. The paper also discusses variants of the actuator-individual normalized FX LMS algorithm.

Place, publisher, year, edition, pages
Blekinge Tekniska Högskola Forskningsrapport, ISSN 1103-1581 ; 16
Keyword [en]
active noise control, multiple reference algorithm, lms algorithm, newton, aircraft
National Category
Signal Processing
URN: urn:nbn:se:bth-00148Local ID: diva2:838441
Available from: 2012-09-18 Created: 2000-12-14 Last updated: 2015-06-30Bibliographically approved

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Johansson, SvenClaesson, Ingvar
Signal Processing

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