• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.551-554
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• 2005

The method of the reduction of the automotive induction noise can be classified by the method of passive control and the method of active control. However, the passive control method has a demerit to reduce the effect of noise reduction at low frequency (below 500Hz) range and to be limited by a space of the engine room. Whereas, the active control method can overcome the demerit of passive control method. The algorithm of active control is mostly used the LMS (Least-Mean-Square) algorithm because the LMS algorithm can easily obtain the complex transfer function in real-time. Especially, When the Filtered-X LMS (FXLMS) algorithm is applied to an ANC system. However, the convergence performance of LMS algorithm goes bad when the FXLMS algorithm is applied to an active control of the induction noise under rapidly accelerated driving conditions. Thus Normalized FXLMS algorithm was developed to improve the control performance under the rapid acceleration. The advantage of Normalized FXLMS algorithm is that the step size is no longer constant. Instead, it varies with time. But there is one additional practical difficulty that can arise when a nonstationary input is used. If the input is zero for consecutive samples, then the step size becomes unbounded. So, in order to solve this problem. the Correlation FXLMS algorithm was developed. The Correlation FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Correlation FXLMS Is presented in comparison with that of the other FXLMS algorithms based on computer simulations.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.245-246
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• 2008

The Correlation FXLMS (Co-FXLMS) algorithm was developed to improve the control performance. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Modified Co-FXLMS is presented in comparison with that of the CO-FXLMS algorithm. Simulation results show that active noise control using Modified Co-FXLMS is effective to control performance of algorithm and prevent divergence.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.3
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• pp.284-292
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• 2008

The active control technique mostly uses the least-mean-square(LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time, particularly when the Filtered-X LMS(FXLMS) algorithm is applied to an active noise control(ANC) system. However, FXLMS algorithm has the demerit that stability of the control is decreased when the step size become larger but the convergence speed is faster because the step size of FXLMS algorithm is fixed. As a result, the system has higher probability which the divergence occurs. Thus the Co-FXLMS algorithm was developed to solve this problem. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Co-FXLMS algorithm is presented in comparison with the FXLMS algorithm. Simulation and experimental results show that active noise control using Co-FXLMS is effective in reducing the noise in duct system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.598-603
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• 2007

The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time, particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, FXLMS algorithm has the demerit that stability of the control is decreased when the step size become larger but the convergence speed is faster because the step size of FXLMS algorithm is fixed. As a result, the system has higher probability which the divergence occurs. Thus the Co-FXLMS algorithm was developed to solve this problem. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Co-FXLMS algorithm is presented in comparison with the FXLMS algorithm. Simulation results show that active noise control using Co-FXLMS is effective in reducing the noise in duct system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.737-742
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• 2007

The method of the reducing duct noise can be classified by passive and active control techniques. However, passive control has a limited effect of noise reduction at low frequencies (below 500Hz) and is limited by the space. On the other hand, active control can overcome these passive control limitations. The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, the convergence performance of the LMS algorithm decreases slightly so it may delay the convergence time when the FXLMS algorithm is applied to the active control of duct noise. Thus the Co-FXLMS algorithm was developed to improve the control performance in order to solve this problem. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Co-FXLMS algorithm is presented in comparison with the FXLMS algorithm. Simulation results show that active noise control using Co-FXLMS is effective in reducing duct noise.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.10
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• pp.1489-1495
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• 2012

In this study, the active noise control experiment has been performed using induction motor noises. While the noises were measured, a induction motor was operated in different speed. For the simulation of ANC(Active Noise Control), test-bed is composed a multi-channel ANC system was constructed. In order to compare the control performance, we performed noise reduction simulations of ANC by Co-FXLMS algorithm and FXLMS algorithm. Through the simulation results, we confirmed that convergence performance and noise decrease effect of the proposed Co-FXLMS algorithm have been improved from existing FXLMS algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.39-40
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• 2007

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.251-252
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• 2007

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.644-646
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• 1998

The main drawback of filtered-X LMS(FXLMS) algorithm for the ANC of broadband noises is its low convergence speed when the filtered reference signals are strongly correlated, producing a large eigenvalue spread in correlation matrix. This correlation can be caused either by autocorrelation of the signals of the reference sensors, or by coupling between the error path which introduces intercorrelation in the filtered reference signals. In this paper, we introduce a transform domain FXLMS(TD-FXLMS) algorithm that has a high convergence speed by orthogonal transform's decorrelation properties.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.260-265
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• 2014

Recently, many active noise control (ANC) systems, which employ the adaptive filter controlling method, have been reported for eliminating unwanted noise. ANC systems based on the filtered-X least mean square (FXLMS) algorithm have a problem with compensating the acoustic feedback of secondary route. It is difficult to apply the real time, because transfer function of secondary route must be measured by off-line method to solve this problem. In this paper, we propose the ANC system that applies a correlation LMS(CLMS) algorithm for improving a problem of transfer function measurement. The proposed algorithm is based on input of road noise. The proposed ANC systems have an advantage of real-time process without degradation of performance, although there are many calculation compared with FXLMS algorithm.