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.

Active interior noise control for rail vehicle using a variable step-size median-LMS algorithm

2018 ◽  
Vol 109 ◽  
pp. 15-26 ◽  
Author(s):  
H. Guo ◽  
Y.S. Wang ◽  
N.N. Liu ◽  
R.P. Yu ◽  
H. Chen ◽  
...  
Keyword(s):  
Noise Control ◽  
Interior Noise ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Rail Vehicle ◽  
Variable Step

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).

A Variable Step Size LMS Based on Sparsity for System Identification

2013 ◽  
Vol 475-476 ◽  
pp. 1060-1066
Author(s):  
X.Q. Chen ◽  
Hua Ju ◽  
Wei Fan ◽  
W.G. Huang ◽  
Z.K. Zhu
Keyword(s):  
Steady State ◽  
System Identification ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Least Mean Square ◽  
Step Size ◽  
Practical Applications ◽  
Variable Step ◽  
Size Adjustment ◽  
State Error

In many practical applications, the impulse responses of the unknown system are sparse. However, the standard Least Mean Square (LMS) algorithm does not make full use of the sparsity, and the general sparse LMS algorithms increase steady-state error because of giving much large attraction to the small factor. In order to improve the performance of sparse system identification, we propose a new algorithm which introduces a variable step size method into the Reweighted Zero-Attracting LMS (RZALMS) algorithm. The improved algorithm, whose step size adjustment is controlled by the instantaneous error, is called Variable step size RZALMS (V-RZALMS). The variable step size leads to yielding smaller steady-state error on the premise of higher convergent speed. Moreover, the sparser the system is, the better the V-RZALMS performs. Three different experiments are implemented to validate the effectiveness of our new algorithm.

A Novel Variable Step Size LMS Algorithm Based on Modified Hyperbolic Tangent and Its Simulation

2012 ◽  
Vol 490-495 ◽  
pp. 1426-1430 ◽  
Author(s):  
Fu Qing Tian ◽  
Rong Luo
Keyword(s):  
Steady State ◽  
Noise Suppression ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Hyperbolic Tangent ◽  
Step Size ◽  
Low Snr ◽  
Variable Step ◽  
Overall Performance ◽  
Simulation Results

In the paper, a new variable step size LMS algorithm based on modified hyperbolic tangent is presented. In the algorithm, the step size is adjusted by the estimation of the autocorrelation between and .The algorithm introduces the compensation monomial to improve the convergence and the parameters to improve the shape and bottom characteristic of hyperbolic tangent. Therefore, the algorithm has faster convergence, better performance of noise suppression，lower steady state error and misadjustment. The theoretical analysis and simulation results all show that the overall performance of the new algorithm exceeds greatly some existent others under low SNR condition.

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