Simulations of an Internal Model-Based Active Noise Control System for Suppressing Periodic Disturbances

1998 ◽  
Vol 120 (1) ◽  
pp. 111-116 ◽  
Author(s):  
Mingsian R. Bai ◽  
Tenyau Wu
Keyword(s):  
Internal Model ◽  
Noise Control ◽  
Active Noise Control ◽  
Robust Stabilization ◽  
Internal Model Principle ◽  
Model Based ◽  
Periodic Disturbances ◽  
System A ◽  
Active Noise ◽  
Infinite Loop

This study proposes an active noise control algorithm using the internal model principle. The structure of the noise is built in the transfer function of the controller in the unity feedback configuration. This method is particularly effective in suppressing periodic disturbance in spite of its simplicity. Perfect disturbance rejection at selected frequencies is achieved by the infinite loop gain of the system. A simulation was performed to investigate the internal model-based algorithm. The guidelines of choosing the control parameters with reference to a robust stabilization theory are summarized.

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.

Model-based active noise control using neural networks

2001 ◽  
Author(s):  
J. M. Concinnila ◽  
J. M. Sousa ◽  
M. Ayala Botto ◽  
J. Sa da Costa
Keyword(s):  
Neural Networks ◽  
Noise Control ◽  
Active Noise Control ◽  
Model Based ◽  
Active Noise

scholarly journals Internal Model Control for a Light-Weight Active Noise-Reducing Casing

2016 ◽  
Vol 41 (2) ◽  
pp. 315-322 ◽  
Author(s):  
Krzysztof Mazur ◽  
Marek Pawełczyk
Keyword(s):  
Internal Model ◽  
Noise Control ◽  
Reference Signal ◽  
Active Noise Control ◽  
Internal Model Control ◽  
Classical Solutions ◽  
Light Weight ◽  
Active Noise ◽  
Model Control ◽  
Global Reduction

Abstract The active noise-reducing casing developed and promoted by the authors in recent publications have multiple advantages over other active noise control methods. When compared to classical solutions, it allows for obtaining global reduction of noise generated by a device enclosed in the casing. Moreover, the system does not require loudspeakers, and much smaller actuators attached to the casing walls are used instead. In turn, when compared to passive casings, the walls can be made thinner, lighter and with much better thermal transfer than sound-absorbing materials. For active noise control a feedforward structure is usually used. However, it requires an in-advance reference signal, which can be difficult to be acquired for some applications. Fortunately, usually the dominant noise components are due to rotational operations of the enclosed device parts, and thus they are tonal and multitonal. Therefore, it can be adequately predicted and the Internal Model Control structure can be used to benefit from algorithms well developed for feedforward systems. The authors have already tested that approach for a rigid casing, where interaction of the walls was significantly reduced. In this paper the idea is further explored and applied for a light-weight casing, more frequently met in practice, where each vibrating wall of the casing influences all the other walls. The system is verified in laboratory experiments.

Nonlinear Active Noise Control via Model-Based Approaches

2011 ◽  
Vol 15 (7) ◽  
pp. 854-859
Author(s):  
Sam Chau Duong ◽  
◽  
Hiroshi Kinjo ◽  
Naoki Oshiro
Keyword(s):  
Noise Control ◽  
Active Noise Control ◽  
Computational Cost ◽  
Lms Algorithm ◽  
Road Traffic Noise ◽  
Backpropagation Algorithm ◽  
Time Step ◽  
Research Attention ◽  
Model Based ◽  
Active Noise

Active noise control has attracted much research attention due to its several advantages over passive noise control. This paper introduces two model-based noise canceling techniques, that is, using the Moving Average (MA) model and a feedforward Neural Network (NN) to estimate the signal. The Least Mean Square (LMS) algorithm is used to minimize the error in the MA model while a backpropagation algorithm is employed to optimize the NN. Due to its advantages of good robustness and nonlinear processing, the NN is considered to be suitable for nonlinear signals. In order to reduce computational cost, the backpropagation algorithm in the NN is applied once at each time step with only one iteration. To examine the methods, two real-world problems are considered, one being engine noise and the other road traffic noise. A comparison between the two methods is carried out. Results indicate that both the MA and NN processors are effective in reducing the noises and that the NN based approach is superior over the MA model, especially for low frequency band.

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