An active impulsive noise control algorithm with a post-adaptive filter and variable step size

2021 ◽  
Vol 150 (5) ◽  
pp. 3238-3250
Author(s):  
Shanjun Li ◽  
Guoyong Jin ◽  
Yukun Chen ◽  
Tiangui Ye
Keyword(s):  
Adaptive Filter ◽  
Control Algorithm ◽  
Noise Control ◽  
Impulsive Noise ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step

Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms

2019 ◽  
Vol 67 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Ningning Liu ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Hui Guo ◽  
Bin Gao ◽  
...  
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Convergence Speed ◽  
Interior Noise ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Variable Step

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

Improved stability performance of a feedback active noise control using a Steiglitz-McBride adaptive notch filter and robust secondary path identification based on variable step size Griffiths LMS algorithm

2021 ◽  
Vol 69 (2) ◽  
pp. 136-145
Author(s):  
S. Roopa ◽  
S.V. Narasimhan
Keyword(s):  
Noise Control ◽  
Active Noise Control ◽  
Notch Filter ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Adaptive Notch Filter ◽  
Active Noise ◽  
Variable Step ◽  
Feedback Active Noise Control

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).

Adaptive IIR Filter Using Variable Step Size for Active Noise Control Inside a Short Duct

2008 ◽  
Author(s):  
Ho-Wuk Kim ◽  
Sang-Kwon Lee
Keyword(s):  
Control System ◽  
Noise Control ◽  
Active Noise Control ◽  
Fir Filter ◽  
Variable Step Size ◽  
Step Size ◽  
Iir Filter ◽  
Active Noise ◽  
Lms Filter ◽  
Variable Step

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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