• 한국소음진동공학회논문집
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• 제18권3호
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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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계학술대회논문집
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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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 추계학술대회논문집
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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.

• 전기학회논문지
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• 제61권10호
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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.

• 한국정밀공학회지
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• 제20권11호
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• pp.100-107
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• 2003

This paper represents a disturbance compensation control for attenuating disturbance responses. In the consideration of the requirements on the model accuracy in the model based compensator designs, an experimental feed forward compensator design based on adaptive estimation by Filtered-x least mean square (FXLMS) algorithm is proposed. The convergence properties of the FXLMS algorithm are discussed and its conditions for the asymptotic convergence are derived theoretically. The effectiveness of the proposed method and the theoretical proof are verified by computer simulation.

• 전력전자학회논문지
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• 제4권6호
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• pp.589-600
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• 1999

사무실에서 사용되는 소용량 무정전진원 장치(UPS : Uninterruptible Power Supply)는 대부분의 소음이 냉각팬을 통하여 발생되고 있는데, 이와 같이 장치의 여러 방향으로 방사되는 소음의 능동적 소음제어를 위해서는 FXLMS(Filtered-x LMS) 알고리즘에 기초를 둔 다중채널 방식의 FXLMS(multiple-channel FXLMS) 알고리즘의 사용이 적당하다. 본 논문에서는 사무실에서 주로 사용되는 소용량 UPS의 소음 발생 특성을 분석하고 다중채널 방식 MRMO(Multiple-Reference/Multiple-Output) FXLMS 알고리즘을 적용하여 방사상으로 전파되는 UPS의 소음을 저감시키기 위한 능동 소음 제어를 수행하고 이의 소음저감 특성을 고찰한다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.780-783
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• 2004

Recently Intake noise has been extensively studied to reduce the engine noise. In order to diminish intake noise several resonators were added to the intake system. However this can cause a reduction of engine output power and an increase of fuel consumption. In this study, active noise control simulation of the Filtered-x LMS algorithm is applied real instrumentation intake noise data under rapid acceleration because intake noise is more excessively increased under the such a harsh condition. But the FXLMS algorithm has poor control performance when the system is disturbed. Thus modified FXLMS algorithm using L-point running average filter is developed to improve the control performance under the rapid acceleration and disturbance. The noise reduction quantity of modified Filtered-x LMS algorithm is more than original one in two cases. In the case of control for real instrumentation intake noise data, maximum residual noise of modified FXLMS algorithm is 2.5 times less than applied the FXLMS and also in the case of disturbed, the modified FXLMS algorithm shows excellent control performance but FXLMS algorithm cat not control.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 춘계학술대회논문집
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• pp.488-493
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• 2005

In gun stabilization systems, the torque comes from the unbalance mass of gun and the base acceleration is an important source of disturbance which degrades stabilization performance. Fatigue of gear train is another important factor affecting structural safety problems. In this paper, a feedback control gain is designed by optimal control weighting to difference between motor and gun velocity, and a feedforward controller using FXLMS algorithm is adopted to investigate those problems. Experimental results show that the feedforward compensator based on FXLMS can reduce the disturbance effects. The directional convergence property according to initial conditions of the FXLMS is also shown through experiments.