Optimal Variable Step-Size NLMS Algorithms With Auxiliary Noise Power Scheduling for Feedforward Active Noise Control

2008 ◽  
Vol 16 (8) ◽  
pp. 1383-1395 ◽  
Author(s):  
A. Carini ◽  
S. Malatini
2021 ◽  
Vol 69 (2) ◽  
pp. 136-145
Author(s):  
S. Roopa ◽  
S.V. Narasimhan

A stable feedback active noise control (FBANC) system with an improved performance in a broadband disturbance environment is proposed in this article. This is achieved by using a Steiglitz-McBride adaptive notch filter (SM-ANF) and robust secondary path identification (SPI) both based on variable step size Griffiths least mean square (LMS) algorithm. The broadband disturbance severely affects not only FBANC input synthesized but also the SPI.TheSM-ANFestimated signal has narrowband component that is utilized for the FBANC input synthesis. Further, the SM-ANF error has broadband component utilized to get the desired signal for SPI. The use of variable step size Griffiths gradient LMS algorithm for SPI enables the removal of broadband disturbance and non-stationary disturbance from the available desired signal for better SPI. For a narrowband noise field, the proposed FBANC improves the convergence rate significantly (20 times) and the noise reduction from 10 dB to 15 dB (50%improvement) over the conventional FBANC (without SM-ANF and variable step size Griffiths LMS adaptation for SPI).


Author(s):  
Ho-Wuk Kim ◽  
Sang-Kwon Lee

FIR filter for a adaptive filter algorithm, is mostly used for an active noise control system. However, FIR filter needs to have more large size of the filter length than it of IIR filter. Therefore, the control system using FIR adaptive filter has slow calculation time. In the active noise control system of the short duct, the reference signal can be affected by the output signal, so IIR filter for ARMA system can be more suitable for the active noise control of the short duct than FIR filter for MA system. In this paper, the recursive LMS filter, which is adaptive IIR filter, is applicated for the active noise control inside the short duct. For faster convergence and more accurate control, a variable step size algorithm is introduced for this recursive LMS filter (R-VSSLMS filter). Using this algorithm and considering the secondary path, the filtered-u R-VSSLMS is conducted successfully on the real experiment in the short duct. The performance of the active control using the filtered-u R-VSSLMS filter, is compared with the performance of the active control using a filtered-x LMS filter.


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