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

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

All-pass filtered x least mean square algorithm for narrowband active noise control

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.

In filtered-x LMS (FxLMS) single-channel feedforward active noise control (ANC) systems, a reference signal is available that is correlated with the primary disturbance at the error microphone. In some practical situations, there may also be a disturbance uncorrelated with the primary disturbance at the error microphone, for which a correlated reference signal is not available. This disturbance, being uncorrelated with the primary noise, cannot be controlled by the standard FxLMS algorithm, and increases the residual noise. In this paper we propose an improved hybrid ANC system that can simultaneously control both the correlated and uncorrelated noise signals. The proposed method comprises three adaptive filters: 1) the FxLMS-based ANC filter to cancel the primary noise; 2) a separate FxLMS-based ANC filter to cancel the uncorrelated disturbance; and 3) an LMS-based supporting adaptive filter to generate appropriate signals for the two ANC filters. Computer simulations demonstrate that the proposed method can effectively mitigate the correlated and uncorrelated primary disturbances. This improved performance is achieved at only a small increase in computational complexity.

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

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.

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.

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 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.

This paper analyzes a narrowband feedforward active noise control (ANC) system using internally synthesized sinusoid as the reference signal. The effect of frequency error between the primary noise and the reference signal on the performance of the ANC system is evaluated in terms of noise reduction in steady state. Theoretical analysis shows that the achievable noise reduction is decreased for increased frequency error, but can be improved by using larger step size. These theoretical results are verified by computer simulations of the narrowband ANC system with transfer functions measured from a real experimental setup.

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.

The fixed tap-length and step-size filtered-X least mean-square (FxLMS) algorithm is conventionally used in active noise control (ANC) systems. A tradeoff between the performance and the convergence rate is a well-known problem due to the choice of the step size. Although the variable-step-size FxLMS algorithms have been proposed for fast convergence, a long tap-length filter is frequently required in order to deal with different environments such that the convergence rate is still subject to a small step size for the long tap length. In this paper, we study a new ANC system with a variable tap-length and step-size FxLMS algorithm. Based on the assumption of an unsymmetric and two-sided exponential decay response model for the ANC control filter, the new FxLMS algorithm has the minimum mean-square deviation for the optimal filter coefficients. In the online secondary path modeling ANC system, simulation results show that the new algorithm with different kind of variable step sizes can provide significant improvements of convergence rate and noise reduction ratio, compared to the fixed-tap-length FxLMS algorithms.

Particle swarm optimization based novel adaptive step-size FxLMS algorithm with reference signal smoothing processor for feedforward active noise control systems

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.

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.