Feedforward Active Noise Control With a New Variable Tap-Length and Step-Size Filtered-X LMS Algorithm

The intake system in an automotive engine has a short duct compared with that of the exhaust system. The filtered-x LMS (FX-LMS) algorithm has been applied to the active noise control (ANC) system in a short acoustic duct. This algorithm design is based on the FIR (finite impulse response) filter; however, it has a slow convergence issue due to a large number of zero coefficients. To improve the convergence performance, the step size of the LMS algorithm was modified from fixed to variable. However, this algorithm is still not suitable for the ANC system of a short acoustic duct because the reference signal is affected by the backward acoustic wave propagated from a secondary source. Therefore, the recursive filtered-u LMS algorithm (FU-LMS) based on the infinite impulse response (IIR) is developed to consider backward acoustic propagation. Generally, this algorithm has a stability problem. The stability issue was improved using an error-smoothing filter. In this paper, the recursive LMS algorithm with a variable step size and smoothing error filter is designed. This recursive LMS algorithm, the FU-VSSLMS algorithm, uses an IIR filter. With fast convergence and good stability, this algorithm is suitable for the ANC system in a short acoustic duct, such as the intake system of an automotive engine. This algorithm is applied to the ANC system of a short acoustic duct. The disturbance signals used as primary noise source are a sinusoidal signal embedded in white noise and the chirp signal, which has a variable instantaneous frequency. The test results demonstrate that the FU-VSSLMS algorithm has a superior convergence performance when compared with the FX-LMS and FX-LMS algorithms. The algorithm can be successfully applied to the ANC system in a short duct, such as the intake duct.

Impulsive noise reduction is a special problem raised in active noise control (ANC) systems. The filtered-x least mean square (FxLMS) algorithm is a typical ANC method that has been successfully applied in practical applications. Some variants such as the logarithmic FxLMS and modified FxLMS algorithms have been proposed to deal with the impulsive noise for ANC. However, those methods require considerable computational loading in real-time applications. In this paper, a modified median FxLMS method is proposed for its low complexity to avoid the effect of the impulsive noise. Simulation results show that the proposed algorithm has better averaged noise reduction performance than the conventional FxLMS and other order statistics based FxLMS algorithms. In addition, the computational complexity stays the minimum requirement through using only the ordering operation, which is very suitable for the real-time implementation.

Active control for vehicle interior noise based on DWT-FxLMS algorithm using a piezoelectric feedback system

The method of reducing automotive intake noise can be classified by passive and active control techniques. However, passive control has a limited effect of noise reduction at low frequency range (below 500 Hz) and is limited by the space of the engine room. However, 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 significantly when the FXLMS algorithm is applied to the active control of intake noise under rapidly accelerating driving conditions. Therefore, in this study, the Co-FXLMS algorithm was proposed to improve the control performance of the FXLMS algorithm during rapid acceleration. 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. The performance of the Co-FXLMS algorithm is presented in comparison with that of the FXLMS algorithm. Experimental results show that active noise control using Co-FXLMS is effective in reducing automotive intake noise during rapid acceleration.

Filtered-X least mean square (FXLMS) algorithm is widely used in active noise control (ANC) systems when the secondary path is linear. However, the performance of FXLMS reduces when nonlinearity is present. Leaky FXLMS (LFXLMS) and minimum output variance FXLMS (MOVFXLMS) algorithms are effective in compensating the nonlinearity effects in nonlinear ANC (NANC). When using optimum leakage factors, these algorithms show close performance with benchmark nonlinear FXLMS (NLFXLMS) algorithm. In all three algorithms, the degree of nonlinearity must be known in advance and are usually assumed. In previous works, Tangential Hyperbolic Function NLFXLMS (THF-NLFXLMS) algorithm has been developed whereby the degree of nonlinearity is estimated using tangential hyperbolic function (THF). In this work, the performance of LFXLMS and MOVFXLMS based on optimum leakage factors calculated using the estimated degree of nonlinearity is compared with THF-NLFXLMS for Hammerstein structure. The results show that optimum MOVFXLMS performs similarly to optimum LFXLMS and THF-NLFXLMS.

A robust evolutionary feedforward active noise control system using Wilcoxon norm and particle swarm optimization algorithm

This paper presents an online secondary path modeling method in active noise cancellation based on an enhanced Filtered-X Least Mean Squares (FxLMS) algorithm, implemented on an Altera Cyclone II board. The main specifications of the proposed algorithm are: (1) it avoids overdriving noise in ear by applying a limiter block, (2) it updates online secondary path modeling filter coefficients, utilizes error signal power, and minimizes system error by injecting random white noise at an appropriate time, which can modify the system changes, and (3) it synthesizes the hardware architecture using VHDL Hardware Description language for an Altera Cyclone II board. In this architecture, only 22 of 300 embedded multipliers and 6% of the logical elements in the FxLMS algorithm have been used. The proposed method has used less than 1% of the Field-programmable Gate Arrays’ board memory. The experiments illustrate convincing results in terms of accuracy, convergence speed, and functionality of the designed and implemented system.

Active Noise Control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. Due to its simplicity and less computational complexity, the Filtered-x Least Mean Square (FxLMS) algorithm is the most widely used ANC algorithm which minimizes the mean square error of noise, but it lacks robustness and stability in presence of high impulses. To overcome this limitation, new methods must be investigated. A robust adaptive algorithm (Bhagyashri algorithm) for impulsive noise suppression is already proposed in literature. In this paper, Bhagyashri algorithm is tested in ANC domain and thus Filtered-x Bhagyashri (FxBhag) algorithm is proposed. Computer simulations are executed to verify the enhanced performance of the FxBhag algorithm. The statistics of impulsive noise is modeled by Symmetric α-stable (SαS) distributions. The suggested solution exhibits better stability and faster convergence with almost same computational complexity as that of standard FxLMS algorithm and its variants.

An enhanced multiple-input multiple-output (MIMO) filtered-x least mean square (FXLMS) algorithm using improved virtual secondary path is proposed as the basis for an active noise control (ANC) system for treating vehicle powertrain noise. This new algorithm is developed to overcome the limitation caused by the frequency-dependent property of the standard FXLMS algorithm and to reduce the variation of convergence speed inherent in multiple-channel cases, in order to improve the overall performance of the control system. In this study, the convergence property of the proposed algorithm is analyzed in the frequency domain in order to yield a better understanding of the physical meaning of the virtual secondary path. In practice, because of the arrangement and sensitivities of the actuators (speakers), transducers (microphones), and physical environment, the magnitude response of the main secondary paths can be very different from each other. This difference will cause difficulty in the overall convergence of the algorithm, which will result in minimal attenuation at some of the channels. The proposed channel equalized (CE) virtual secondary path algorithm is designed to tackle this difficulty by equalizing the mean magnitude level of the main secondary paths and by adjusting other secondary paths correspondingly to keep the coupling effects among the control channels unchanged. The performance of the proposed algorithm is validated by analyzing a two-input two-output active powertrain noise control system.

The noise in the marine engine room has always been a major cause of disturbance and damage to sailors’ physical and mental health; however, the antinoise countermeasures were being ignored by the industry. To further reduce the noise in the marine engine room, the system structure of active noise control (ANC) and the theoretical basis of the least mean square (LMS) and filtered X least mean square (FXLMS) algorithm are studied. Based on the FXLMS algorithm ANC system, the simulation study of active noise control in the marine engine room of a multigas carrier is carried out. According to the simulation results by the diagrams plotted on the time domain and spectrum, the ANC systems with the FXLMS algorithm can effectively reduce the noise in the marine engine room by about 20 dB, especially for the peak low-frequency range of 20–500 Hz with a large antinoise effect.

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.

Power transformers are known to generate noise, however, active noise emanating from power transformers many at time is unbearable and has been a subject of concern overtime in power engineering. Several studies on it have principally centered on single channel active noise controller (scANC) using fixed step size, which are characterized with the problems such as signal congestion and instability. This study, therefore, employed Filtered-X Least Mean Square (FXLMS) on a dual channel active noise controller (dcANC) using variable step-size. The noise emanating from a 100 MVA, 132/33 kV power transformer was captured with the help of a smart phone in flight mode in accordance with IEC 60076-10 standard of 2 m away from the transformer and 1 m apart between each measurement. The recorded noises were taken at one-third of the height of the transformer tank, while electromagnetic interference from the phone and others were assumed to be negligible. A dcANC with FXLMS was model mathematically and implemented in Simulink in the MATLAB environment. Noise reduction ratio, loudness unit full scale and mean square error were used as performance metrics. The simulation results obtained showed that the original noise emanating from the power transformer when ANC was not used was found to be 70 dB. When scANC with FXLMS was used, the noise was reduced to 30.55 dB whereas, when dcANC with the FXLMS was employed it was reduced to 0.19 dB. Also, the MSE value of -72 dB was obtained in the proposed dcANC with FXLMS, compared with -64 dB obtained from scANC with FXLMS algorithm. The results of the simulation using FXLMS on both scANC and dcANC showed that the performance of the dcANC is comparatively better in term of the stated performance metrics.

Excessive noise levels inside infants incubators in neonatal intensive care units (NICU) contribute to number of harmful effects on the infant's health. This paper develops and implements practical active noise control (ANC) systems for the infant incubators. The filtered-X least mean square (FXLMS) algorithm is used to cancel the noise inside the incubator. The multi-channel and pseudo multi-channel ANC systems are proposed to enhance the noise cancellation performance in terms of cancellation gain and quiet zone. An experimental setup based on real Giraffe incubator from GE Healthcare is used for real-time experiments. The results show that the ANC system can dramatically reduce the noise and cost effective.

Cascading time-frequency domain filtered-x LMS algorithm for active control of uncorrelated disturbances

The notable advantages of Filtered-x Least Mean Square (FXLMS) Algorithm over other algorithms in Active Noise Control (ANC) environment are its simple time realization, Low computational complexity, robustness and simplicity though the algorithm has slow convergence. In order to optimize its frequency dependent convergence value, a convergence parameter is used. Thus upon application of this algorithm to noise cancellation techniques especially when the noise contains multiple tones, the optimization of the convergence parameter can only be done for one of those tones. The rest of the tones will have a convergence rate slower than the tone for which the convergence parameter is optimized. Again the remaining tones will have less attenuation than they would have if they were to be optimized separately for different tones. In this paper, two more filters are added to the existing structure of the FXLMS algorithm. The concept of Soft Threshold is also introduced in order to achieve better performance in terms of speed of convergence. Furthermore the proposed algorithm is applied to noise cancellation having both Single tone and Multi tone noise and the results are compared with the existing FXLMS and Filtered-X Recursive Least Square algorithm.