Hybrid Active Noise Control of a One-Dimensional Acoustic Duct

2001 ◽  
Vol 124 (1) ◽  
pp. 10-18 ◽  
Author(s):  
E. Esmailzadeh ◽  
A. Alasty ◽  
A. R. Ohadi
Keyword(s):  
Noise Control ◽  
Active Noise Control ◽  
Closed Form Solution ◽  
Form Solution ◽  
Least Mean Square ◽  
One Dimensional ◽  
Acoustic Feedback ◽  
Active Noise ◽  
Adaptive Weight ◽  
Fxlms Algorithm

Based on the closed-form solution of a one-dimensional wave equation, the primary, secondary and acoustic feedback paths for the active control of sound in an acoustic duct have been investigated. Accurate models for the condenser microphone and loudspeaker, which include both the electro-mechanical and mechano-acoustical couplings as well as acoustical damping, have been considered. A generalized form of the filtered-x least mean square (FXLMS) algorithm that uses a more general recursive adaptive weight update equation to improve the performance of the FXLMS algorithm has been developed. Computer simulations were carried out to investigate the performance of acoustical feedback and feedback neutralization as well as the effect of boundary conditions on the performance of active noise control (ANC) systems. Comparisons of the simulation results were carried out.

Active Noise Cancellation Using Feedforward and Hybrid Controls

2001 ◽  
Author(s):  
A. R. Ohadi ◽  
E. Esmailzadeh ◽  
A. Alasty
Keyword(s):  
Active Noise Control ◽  
Closed Form Solution ◽  
Form Solution ◽  
Time Shift ◽  
One Dimensional ◽  
Acoustic Feedback ◽  
Active Noise ◽  
Adaptive Weight ◽  
Fxlms Algorithm ◽  
Overall Performance

Abstract The exact closed-form solution of a one-dimensional wave equation including the viscous damping effect has been obtained from the Green’s function. Accurate models for the error sensor and secondary loudspeaker, which includes the electro-mechanical and mechano-acoustical couplings, have been used and the transfer function of the primary, secondary and acoustic feedback paths of the active noise control system have been obtained. The generalized form of the classical FXLMS algorithm, referred to G-FXLMS algorithm, has been developed. In contrast to the FXLMS algorithm, G-FXLMS algorithm does not neglect the time shift of the filter coefficients and employs a more general recursive adaptive weight update equation, which can improve the performance of FXLMS algorithm. Simulation results presented to compare the performance of the feedforward and hybrid ANC systems and to study the effect of acoustical feedbacks and boundary conditions on the overall performance of ANC systems.

scholarly journals Fuzzy logic feedforward active noise control with distance ratio and acoustic feedback using Takagi–Sugeon–Kang inference

2019 ◽  
Vol 39 (1) ◽  
pp. 174-189
Author(s):  
Tongrui Peng ◽  
Quanmin Zhu ◽  
M. Osman Tokhi ◽  
Yufeng Yao
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Control ◽  
Noise Control ◽  
Active Noise Control ◽  
Distance Ratio ◽  
Acoustic Feedback ◽  
Logic Control ◽  
Active Noise ◽  
Fxlms Algorithm

Noise, as undesired sound, severely affects the quality of human life. Currently, active noise control method has demonstrated its capability in low-frequency noise cancellation and the advance in saving money and reducing weight and volume of related materials used in the passive noise control technology. The widespread configuration for active noise control technology is finite impulse response filter with filtered-x least mean squares (FxLMS) algorithm. However, the nonlinearities in the secondary path, which mainly arise from sensors, actuators and amplifiers used in the active noise control system, will cause instability and degrade the performance while using the FxLMS algorithm. In order to cope with this challenge, many new approaches have been proposed and fuzzy logic control is one of these. In this paper, a Takagi–Sugeon–Kang-type fuzzy logic control-based feedforward active noise control system with focus on the geometry configuration is introduced. In contrast to previous work, all physical paths are modelled by pure time delay transfer function and the acoustic feedback is added as part of inputs for the fuzzy logic control. Computational experiments are implemented within the Matlab/Simulink platform, and several case studies are presented with time and frequency domain analyses to demonstrate the cancellation ability of the proposed feedforward active noise control system and investigate the influence of distance ratio on the overall noise cancellation performance.

scholarly journals Comparison of Performance and Computational Complexity of Nonlinear Active Noise Control Algorithms

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Mouayad A. Sahib ◽  
Raja Kamil
Keyword(s):  
Computational Complexity ◽  
Noise Control ◽  
Active Noise Control ◽  
Practical Implementation ◽  
Mean Square ◽  
Least Mean Square ◽  
Computational Load ◽  
Active Noise ◽  
Nonlinear Algorithms ◽  
Fxlms Algorithm

Research on nonlinear active noise control (NANC) revolves around the investigation of the sources of nonlinearity as well as the performance and computational load of the nonlinear algorithms. The nonlinear sources could originate from the noise process, primary and secondary propagation paths, and actuators consisting of loudspeaker, microphone or amplifier. Several NANCs including Volterra filtered-x least mean square (VFXLMS), bilinear filtered-x least mean square (BFXLMS), and filtered-s least mean square (FSLMS) have been utilized to overcome these nonlinearities effects. However, the relative performance and computational complexities of these algorithm in comparison to FXLMS algorithm have not been carefully studied. In this paper, systematic comparisons of the FXLMS against the nonlinear algorithms are evaluated in overcoming various nonlinearity sources. The evaluation of the algorithms performance is standardized in terms of the normalized mean square error while the computational complexity is calculated based on the number of multiplications and additions in a single iteration. Computer simulations show that the performance of the FXLMS is more than 80% of the most effective nonlinear algorithm for each type of nonlinearity sources at the fraction of computational load. The simulation results also suggest that it is more advantageous to use FXLMS for practical implementation of NANC.

scholarly journals Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule

2009 ◽  
Vol 16 (3) ◽  
pp. 325-334 ◽  
Author(s):  
Ya-li Zhou ◽  
Qi-zhi Zhang ◽  
Tao Zhang ◽  
Xiao-dong Li ◽  
Woon-seng Gan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Noise Control ◽  
Active Noise Control ◽  
Time Varying ◽  
Mean Square ◽  
Least Mean Square ◽  
Functional Link ◽  
Active Noise ◽  
Artificial Neural

In practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN)-based simultaneous perturbation stochastic approximation (SPSA) algorithm, which functions as a nonlinear mode-free (MF) controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS) algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.

Active Noise Control Based on TMS320C5509

2013 ◽  
Vol 631-632 ◽  
pp. 1172-1176
Author(s):  
Yong Wei Ma ◽  
Xin Ke Gou ◽  
Xian Jun Du ◽  
Chong Yu Ren
Keyword(s):  
Control System ◽  
Real Time ◽  
Noise Control ◽  
Active Noise Control ◽  
Noise Cancellation ◽  
Real Time Control ◽  
Time Control ◽  
Active Noise ◽  
Fxlms Algorithm ◽  
Mixed Language

The feed-forward adaptive active noise control (AANC) system is presented. Firstly, the hardware project of the system is brought forward, by selecting TMS320C5509 DSP as the controller. Then, using the mixed language, the active noise real-time control system is realized, based on the FXLMS algorithm. It’s proved that a good noise cancellation is achieved by the experiment.

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