• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.1
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• pp.40-47
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• 2005

This paper concerns on Filtered-x least mean square (FXLMS) algorithm for adaptive estimation of feedforward control parameters. The conditions for convergence in ensemble mean of the FXLMS algorithm are derived and the directional convergence properties are discussed from a new geometric vector analysis. The convergence and its directionality are verified along with some computer simulations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.447-450
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• 2003

This paper concerns on application of active magnetic bearing(AMB) system to levitate the elevation axis of an electro-optical sight mounted on moving vehicles. In such a system. it is desirable to retain the elevation axis within the predetermined air-gap while the vehicle is moving. A disturbance compensation control is proposed to reduce the base motion response. In the consideration of the uncertainty of the system model, a filtered-x least-mean-square(FXLMS) algorithm is used to estimate adaptively the frequency response function of the feedforward control which cancels disturbance responses. The frequency response function is fitted to an optimal feedforward control. Experimental results demonstrate that the proposed control reduces the air-gap deviation to 27.7% that by feedback control alone.

• 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 KIEE Conference
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• 2004.11c
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• pp.565-567
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• 2004

In active noise control (ANC) systems, the convergence behavior of the conventional Filtered-X Least Mean Square (FXLMS) algorithm may be affected by nonlinear distortions in the secondary path (e.g., in the power amplifiers, loudspeakers, transducers, etc.), which may lead to degradation of the error-reduction performance of the ANC systems. In this paper, a stable FXLMS algorithm with fast convergence is proposed to compensate for undesirable nonlinear distortions in the secondary-path of ANC systems by employing the Volterra filtering approach. In particular, the proposed approach is based on the utilization of the conventional P-th order inverse approach to nonlinearity compensation in the secondary path of ANC systems. Finally, the simulation results showed that the proposed approach yields a better convergence behavior In the nonlinear ANC systems than the conventional FXLMS.

• 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.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.10
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• pp.1712-1719
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• 2003

This paper concerns on application of active magnetic bearing(AMB) system to levitate the elevation axis of an electro-optical sight mounted on moving vehicles. In such a system, it is desirable to retain the elevation axis within the predetermined air-gap while the vehicle is moving. An optimal base acceleration feedforward control is proposed to reduce the base motion response. In the consideration of the uncertainty of the system model, a filtered-x least-mean-square(FXLMS) algorithm is used to estimate the frequency response function of the feedforward control which cancels base motions. The frequency response function is fitted to an optimal feedforward control. Experimental results demonstrate that the proposed control reduces the air-gap deviation to 27.7% that by feedback control alone.

• 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.