Application of a Least Squares Lattice Algorithm to Active Noise Control for an Automobile

1997 ◽  
Vol 119 (2) ◽  
pp. 318-320 ◽  
Author(s):  
Hisashi Sano ◽  
Shuichi Adachi ◽  
Hideki Kasuya
Keyword(s):  
Least Squares ◽  
Noise Control ◽  
Active Noise Control ◽  
Broadband Noise ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
The Road ◽  
Active Noise ◽  
Alternative Approach ◽  
Lattice Algorithm

The purpose of this paper is to propose an alternative approach to active noise control (ANC) using the least squares lattice (LSL) algorithm. Typically, in ANC applications, the least-mean-square (LMS) algorithm has been used because of its simplicity. However, the LMS algorithm has the disadvantage of slow convergence speed in the case of broadband noise, such as the road noise present in the passenger compartment of automobiles traveling on rough road surfaces. In order to solve this problem, the LSL algorithm for ANC is considered. By computer simulation using actual car data, the LSL algorithm proves to be more effective than the LMS one.

scholarly journals Experimental investigation on the potential of the Feedback Fx-LMS Active Noise Control technology for use in industrial generator sets

2021 ◽  
Vol 312 ◽  
pp. 08007
Author(s):  
Marco Ciampolini ◽  
Lorenzo Bosi ◽  
Luca Romani ◽  
Andrea Toniutti ◽  
Matteo Giglioli ◽  
...  
Keyword(s):  
Acoustic Waves ◽  
Noise Control ◽  
Noise Source ◽  
Acoustic Noise ◽  
Active Noise Control ◽  
Lms Algorithm ◽  
Destructive Interference ◽  
Exhaust Pipe ◽  
Least Mean Square ◽  
Active Noise

Active Noise Control (ANC) has been considered a promising technology for the abatement of acoustic noise from the mid-20th century. Feedback and Feedforward ANC algorithms, based on the destructive interference principle applied to acoustic waves, have been developed for different applications, depending on the spectrum of the noise source. Feedback ANC algorithms make use of a single control microphone to measure an error signal which is then employed by an adaptive filter to estimate the noise source and generate an opposite-phase control signal. The Fx-LMS (Filtered-X Least Mean Square) algorithm is mostly adopted to update the filter. Feedback ANC systems have proven to be effective for the abatement of low-frequency quasi-steady noises; however, different challenges must be overcome to realize an effective and durable system for high-temperature application. This paper aims at experimentally assessing the feasibility of a Feedback Fx-LMS ANC system with off-line Secondary Path estimation to be used in mid-size diesel gensets for the reduction of the exhaust noise. Several solutions are proposed, including the mechanical design, the development of the Fx-LMS algorithm in the LabVIEW FPGA programming language, and the key features required to prevent parts from thermal damage and fouling. The developed prototype was implemented on a 50-kW diesel genset and tested in a semi-anechoic chamber. The noise abatement inside the exhaust pipe and at different measurement points around the machine was evaluated and discussed, showing good potential for improving the acoustic comfort of genset users.

Research of Quantum Algorithm for Adaptive Active Noise Control System

2012 ◽  
Vol 457-458 ◽  
pp. 196-201
Author(s):  
Wei Jiang
Keyword(s):  
Control System ◽  
Adaptive Algorithm ◽  
Noise Control ◽  
Active Noise Control ◽  
Quantum Algorithm ◽  
Adaptive Controller ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Active Noise

Adaptive active noise control based on least mean square (LMS) algorithm is a linear adaptive filter so that it cannot obtain desired noise reduction. Quantum algorithm is combined with noise control to form quantum adaptive controller. Quantum adaptive algorithm is discussed completely and noise control system is simulated in order to analyze the effects of noise control.

scholarly journals Modified Filtered-X Hierarchical LMS Algorithm with Sequential Partial Updates for Active Noise Control

2020 ◽  
Vol 11 (1) ◽  
pp. 344
Author(s):  
Pedro Ramos Lorente ◽  
Raúl Martín Ferrer ◽  
Fernando Arranz Martínez ◽  
Guillermo Palacios-Navarro
Keyword(s):  
Computational Complexity ◽  
Convergence Rate ◽  
Noise Control ◽  
Active Noise Control ◽  
Adaptive Strategy ◽  
Lms Algorithm ◽  
Step Size ◽  
Periodic Disturbances ◽  
Active Noise ◽  
Dependent Parameter

In the field of active noise control (ANC), a popular method is the modified filtered-x LMS algorithm. However, it has two drawbacks: its computational complexity higher than that of the conventional FxLMS, and its convergence rate that could still be improved. Therefore, we propose an adaptive strategy which aims at speeding up the convergence rate of an ANC system dealing with periodic disturbances. This algorithm consists in combining the organization of the filter weights in a hierarchy of subfilters of shorter length and their sequential partial updates (PU). Our contribution is threefold: (1) we provide the theoretical basis of the existence of a frequency-dependent parameter, called gain in step-size. (2) The theoretical upper bound of the step-size is compared with the limit obtained from simulations. (3) Additional experiments show that this strategy results in a fast algorithm with a computational complexity close to that of the conventional FxLMS.

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).

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