The application of active noise control to the problem of low frequency engine noise in automobile interiors

1993 ◽  
Vol 94 (4) ◽  
pp. 2453-2453
Author(s):  
Robert F. Deuel
Keyword(s):  
Noise Control ◽  
Active Noise Control ◽  
Low Frequency ◽  
Engine Noise ◽  
Active Noise

Inversion-Based Feedforward Approach to Broadband Acoustic Noise Reduction

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Tom C. Waite ◽  
Qingze Zou ◽  
Atul Kelkar
Keyword(s):  
Noise Control ◽  
Feedforward Control ◽  
Acoustic Noise ◽  
Active Noise Control ◽  
Low Frequency ◽  
Control Technique ◽  
Frequency Range ◽  
Nonminimum Phase ◽  
Control Approach ◽  
Active Noise

In this article, an inversion-based feedforward control approach to achieve broadband active-noise control is investigated. Broadband active-noise control is needed in many areas, from heating, ventilation and air conditioning (HVAC) ducts to aircraft cabins. Achieving broadband active-noise control, however, is very challenging due to issues such as the complexity of acoustic dynamics (which has no natural roll-off at high frequency, and is often nonminimum phase), the wide frequency spectrum of the acoustic noise, and the critical requirement to overcome the delay of the control input relative to the noise signal. These issues have limited the success of existing feedforward control techniques to the low-frequency range of [0,1]kHz. The modeling issues in capturing the complex acoustic dynamics coupled with its nonminimum-phase characteristic also prevent the use of high-gain feedback methods, making the design of an effective controller to combat broadband noises challenging. In this article, we explore, through experiments, the potential of inversion-based feedforward control approach for noise control over the 1kHz low-frequency range limit. Then we account for the effect of modeling errors on the feedforward input by a recently developed inversion-based iterative control technique. Experimental results presented show that noise reduction of over 10–15dB can be achieved in a broad frequency range of 5kHz by using the inversion-based feedforward control technique.

Experimental Study on an Active Noise Control System for Turboprop Driven Aircraft

2013 ◽  
Vol 333-335 ◽  
pp. 2142-2145
Author(s):  
Qing Fu Kong ◽  
Yu Liang Dai ◽  
Shi Jian Zhu ◽  
Jia Ming Wu
Keyword(s):  
Experimental Study ◽  
Control System ◽  
Sound Source ◽  
Noise Control ◽  
Active Noise Control ◽  
Low Frequency ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Experimental Platform ◽  
Active Noise

In order to find a solution to the control of low frequency noise in the cabin of turboprop driven aircraft, an experimental active noise control (ANC) system is introduced in the paper, which consists of vibroacoustic field analogue subsystem, acoustic field measurement subsystem and acoustic barrier exciter subsystem. Effects of both different spaces between the primary sound source and secondary sound source of the ANC system and different frequency noises on noise-reduction result are investigated based on the experimental platform. Results of the experiment show a significant potential of the ANC method for the control of low frequency noise in the cabin of turboprop driven aircraft.

Optimal parametric design of delayless subband active noise control system based on genetic algorithm optimization

2021 ◽  
pp. 107754632110016
Author(s):  
Guo Long ◽  
Yawen Wang ◽  
Teik C Lim
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Noise Control ◽  
Parametric Design ◽  
Active Noise Control ◽  
Low Frequency ◽  
Broadband Noise ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Active Noise

Active noise control systems are generally application-specific, and an appropriate algorithm with an optimal configuration is desirable in the first stage of active noise control system design and deployment. This study presents a design of the subband active noise control system with optimal parameters for a practical broadband active noise control. Although the delayless subband active noise control has gained wide attention for broadband noise cancellation, an optimal design remains a challenge because of the complex interplay between multiple factors such as spectral leakage, delay and weight stacking distortion subject to a number of configurable parameters, and weight stacking method. The configurable parameters can hardly be optimized independently because the active noise control performance depends on the combined configuration. A simple near black box active noise control algorithm optimization model is thus established by incorporating the genetic algorithm optimization into the parametric design of the delayless subband algorithm. The automated process does not require an understanding of the performance characteristics for different parameters. The significance of applying the automated genetic algorithm optimization to the complex multiparameter nonlinear active noise control design is revealed by numerical simulations, particularly for the multichannel low-frequency broadband active noise control system configured with the delayless subband algorithms. This provides a way for the optimal parametric design of subband active noise control before being used in a practical complex scenario.

Simulation of Active Noise Control System on the Truck Interior Cab

2013 ◽  
Vol 798-799 ◽  
pp. 443-447
Author(s):  
Qi Chen LU ◽  
Hui Bin LI ◽  
Hua Huang
Keyword(s):  
Control System ◽  
Control Systems ◽  
Active Control ◽  
Noise Control ◽  
Active Noise Control ◽  
Low Frequency ◽  
Frequency Noise ◽  
Simulink Model ◽  
Active Noise ◽  
Active Control System

Studying on adaptive active noise control (AANC) system of the truck interior cab to reduce the low-frequency noise,a normalization FLMS algorithm simulink model is established in Matlab/Simulink.Then taking it as the core,a feedforward adaptive active control system and a feedback adaptive active control system of the tuck interior cab are established in Matlab/Simulink .Considerating the actual error channels effects on systems ,the noise reduction effects of two adaptive active control systems are verified from the simulintion results.Through comparing the two wo adaptive active control systems,we found that the feedforward adaptive active control system is more stable.

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