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In the conventional hybrid active noise control (CHANC) algorithm, the filtered-x least mean square (FXLMS) algorithm is used in both feedforward and feedback structures. However, the FXLMS algorithm does not process the reference signal and the error signal before updating the weight vector, which leads to the inadequate robustness of the CHANC algorithm in the impulsive noise environment. To solve this problem, the maximum versoria criterion is incorporated into the HANC (MVC-HANC) algorithm in this paper. Firstly, the MVC-HANC algorithm employs a novel nonlinear function to compress the error signal. Secondly, the proposed algorithm uses the modified sigmoid function to constrain the reference signal. To further improve the noise attenuation performance, the MVC-HANC algorithm uses a novel exponential function to adjust the step-size adaptively. Simulation and experiment results demonstrate that the proposed algorithm has the better attenuation performance than the conventional HANC algorithm. [ABSTRACT FROM AUTHOR]

In many scenarios where impulse noise occurs, an active noise control (ANC) system using a filtered-x least mean square (FxLMS) algorithm will have undesirable effects. To solve this problem, a novel adaptive algorithm is proposed that takes the Gaussian error function as the cost function to nonlinearly transform the residual error. To attenuate the different levels of impulse noise, the parameter k is used as a factor that transforms the error signal by different degrees. To further improve the efficiency of the algorithm, we propose a variable step size strategy based on normalized variable step size, combined with the sine function. Not only the random impulse noise signals generated by the Chambers-Mallows-Stuck method are adopted for simulation, but also the noise signals collected from real vehicles are used in this work. Finally, the simulations are carried out and the results show that the proposed algorithm offers perfect performance in dealing with different levels of impulse noise. [ABSTRACT FROM AUTHOR]

Filtered‐x least mean square (FxLMS) algorithm and its variants have been prevalently applied in the field of active noise control. However, these algorithms still suffer from relatively low convergence rates and steady‐state noise reduction. In this article, we propose a new idea to improve the performance of the conventional filtered‐x least mean square/fourth (FxLMS/F) algorithm by weighting reference signal samples, thus a weighted reference signal FxLMS/F (WRS‐FxLMS/F) algorithm is presented. Besides, to solve the contradiction between convergence speed and steady‐state noise reduction, the convex combination method is combined with the WRS‐FxLMS/F algorithm, and a convex combination WRS‐FxLMS/F (CWRS‐FxLMS/F) algorithm is developed. The computational complexity of the proposed algorithms is analyzed, and the simulation results demonstrate that the proposed algorithms have better performance both in the steady‐state noise reduction and convergence rate than the traditional FxLMS, NFxLMS, and FxLMS/F algorithm. [ABSTRACT FROM AUTHOR]

• Proposing the reference signal selection method based on the genetic algorithm (GA). • Proposing a multi-channel VSS-TF-HANC algorithm for active control of road noise. • The proposed algorithm performs excellent noise reduction performance and low computational complexity. • The proposed algorithm achieves good noise reduction performance under complex driving conditions. • The proposed algorithm is able to achieve noise reduction at multiple noise reduction locations. Multi-channel Active Road Noise Control (ARNC) systems require numerous adaptive filters to reduce noise at multiple positions inside the vehicle. However, road noise conditions are more complex, and the correlation between the reference signals and the target noise is often poor. When the driving conditions become slightly complex, the correlation between the reference signal and the target noise is often poor. As a result, traditional road noise control algorithms applied to multi-channel ARNC systems often suffer from high computational cost and poor noise reduction performance in non-steady-state and high-speed driving conditions. To address this issue, a reference signal selection method based on the genetic algorithm (GA) is proposed to obtain a reference signals combination with stronger coherence. On this basis, we introduce the time–frequency domain structure into the forward-feedback hybrid algorithm and a multi-channel time–frequency domain hybrid algorithm with a novel variable step size method (VSS-TF-HANC) is proposed in this paper. The feedforward part adopts a time–frequency domain structure and introduces a novel variable step-size strategy to reduce computational complexity and improve noise reduction performance. The feedback part adopts a control system based on the Internal Model Control (IMC) architecture to attenuate the interior noise components that have a strong response but poor correlation with the reference signals. Simulation results demonstrate that the proposed algorithm achieves excellent control results under complex driving conditions, with significantly better noise reduction performance compared to the control algorithms that solely employ the feedforward structure, while also exhibiting low computational cost. [ABSTRACT FROM AUTHOR]

• A partial subband update strategy is proposed to reduce the computational complexity of the adaptive algorithms. • A weighted reference signal normalized filtered-x least mean square algorithm is designed for subband filters to improve the convergence rate and reduce the round-off error. • A purely time-delayed secondary path is designed to eliminate the negative impact of the magnitude response of the secondary path. • A parameter observer is proposed and a parameter adjusting strategy is designed based on this observer. Road noise is one of the dominant noise sources in pure electric vehicles. Active control for road noise is an attractive solution since this technique is suitable for controlling low-frequency noise. Feed-forward active road noise control (ARNC) systems utilize lots of reference signals and corresponding adaptive filters, and the control performance usually improves as the number of reference signals increases. To reduce the computational cost of ARNC systems so that more reference signals can be processed, we introduce the delayless subband structure into ARNC system and a delayless partial update subband with weighted reference signal normalized filtered-x least mean square (DPUS-WRS-NFxLMS) algorithm is proposed in this paper. Based on the traditional delayless subband algorithm, a partial subband update strategy is proposed to reduce the computational cost. Furthermore, the weighted reference signal is introduced for each subband filter. In this way, a modified vector is designed to assign different weights to different reference signal samples so that the convergence performance can be improved and the round-off error can be reduced. In addition, an adjustment strategy for the step size and reference signal weights is designed to make the algorithm have a better balance between the convergence rate and steady-state behavior. Finally, a purely time-delayed secondary path is constructed as the estimated secondary path to avoid the negative impact of the magnitude-frequency characteristics of the actual secondary path. The simulation results prove that the proposed algorithm achieves better control results than the traditional algorithms with lower computational complexity. [ABSTRACT FROM AUTHOR]

The conventional hybrid active noise control (HANC) system adopts the standard filtered-x least mean square (FxLMS) algorithm to adapt its control filters. However, the constant step size in the algorithm leads to not only the contradiction between convergence rate and steady-state noise reduction, but inadequate robustness under certain noise circumstances. For performance improvement, a new adaptive step-size method is incorporated into the HANC (ASHANC) system to optimize the adaptation of the filters. It combines the strengths of the variable step-size FxLMS (VSFxLMS) algorithm and the modified normalized FxLMS (MNFxLMS) algorithm. Different step-size adjustment strategies can be obtained by setting specific parameters. Furthermore, to obtain the best performance of the system, we develop a method to determine the involved key parameters on the basis of the improved particle swarm optimization (IPSO) algorithm. The objective function of the IPSO algorithm takes the convergence rate and noise reduction into consideration to search for optimum values. Simulation results demonstrate that the proposed ASHANC system achieves faster convergence rate, better noise reduction, and enhanced robustness than the other investigated systems. [ABSTRACT FROM AUTHOR]

• Proposing two innovative AINC algorithms for active control of impulsive noise. • Strong versatility for impulsive noise with different intensities. • Suppressing impulsive samples both from the reference signal path and the error signal path. • Needing neither prior acquisition of characteristic exponent nor thresholds estimation. • Performing fast convergence and great steady-state noise reduction performance. Impulsive noise is widely distributed in various scenarios and becomes an important challenge for the practical applications of active noise control (ANC) system. The conventional ANC algorithms based on the transformation function have a fixed compression level for error signal, leading to slow convergence and weak noise reduction under certain circumstances. To overcome this defect, this paper proposes an enhanced filtered-x arctangent error Least Mean Square (EFxatanLMS) algorithm by designing an adjustable nonlinear transformation function of error signal with arctangent form. Specifically, a compression factor is introduced in the transformation function to govern the compression shape of the function so as to realize ideal effect on impulsive noise with different intensities. For the purpose of further optimizing the capability of the proposed algorithm, an improved normalized step-size EFxatanLMS (NSS-EFxatanLMS) algorithm is proposed. It adopts a novel time-varying normalized function to adjust the step-size coefficient to the appropriate value adaptively. Numerical simulations verify the effectiveness of the proposed algorithms for Gaussian noise and non-Gaussian impulsive noise. [ABSTRACT FROM AUTHOR]

The feedforward hybrid active noise control (HANC) system combines the strengths of the narrowband ANC (NANC) system and the broadband ANC (BANC) system. Taking this advantage into account, this paper applies it to attenuate the vehicle interior noise mixed with narrowband and broadband components. However, in the HANC system, the NANC subsystem and BANC subsystem are fed a common error signal which depends on the combined secondary signals from the both subsystems. This causes the problem of mutual coupling and hence affects the noise reduction performance. To tackle the problem, we propose a modified HANC (MHANC) system in this paper. In the MHANC system, the error signal of each ANC subsystem is independently synthesized on the basis of the estimated primary and secondary signals. Note that the NANC system for engine order noise control suffers performance deterioration when the fluctuating real-time engine speed signal is used for constructing internal reference signals. For performance improvement, an enhanced NANC (ENANC) system is developed by smoothing the fluctuating engine speed signal. Meanwhile,the ENANC system is incorporated in the MHANC system to form an enhanced MHANC (EMHANC) system. Simulations were performed on the vehicle interior noise recorded under actual driving conditions. The corresponding results demonstrate the superiority of the proposed MHANC and EMHANC systems. [ABSTRACT FROM AUTHOR]

The unimproved filtered-x least mean square (FxLMS) algorithm is not enough to guarantee the convergence rate and stability when outliers appear in the reference signal X n . To make up for this deficiency, a novel adaptive step-size FxLMS with reference signal smoothing processor (NASFSxLMS) algorithm is proposed to accelerate the convergence rate and weaken the impact of outliers. The reference signal smoothing processor integrates the Moving-Median filter and the Hampel filter to preprocess different types of reference signals. The novel adaptive step-size is developed by normalizing the step-size adjustment factor which takes the Euclidean Norm of X n and error signal e n into consideration. What's more, different coefficients of the step-size adjustment factor h (n) in the normalization function result in different noise reduction effects. To further ameliorate the noise reduction performance of the proposed algorithm, we combine the NASFSxLMS algorithm with the particle swarm optimization (NASFSxLMS-PSO) algorithm to get the optimal coefficients of h (n). Simulation results demonstrate the faster convergence rate, enhanced stability and higher noise reduction of the proposed algorithms under different types of reference signals. [ABSTRACT FROM AUTHOR]

The weight-constrained filtered-x least mean square (CFxLMS) algorithm with a fixed parameter shows slow convergence speed and weak noise reduction performance under certain circumstances. In order to solve this problem, a generalized modified adaptive CFxLMS (GMACFxLMS) algorithm is proposed to construct an adaptive weight-constrained parameter for the active noise control (ANC) systems. The GMACFXLMS algorithm is developed by using mixed operation of the Euclidean Norm of residual error e n and input noise signal X n . Different noise reduction effect will be achieved by choosing different coefficients of the Euclidean Norm of e n and X n . Especially, it can be utilized to deal with the ANC under impulse noise with symmetric α -stable (S α S) distribution environments. To further improve the performance of the GMACFxLMS algorithm, specifically for high impulse noise, we present an enhanced GMACFxLMS algorithm (EGMACFxLMS) with amplitude constraint of e n and X n . Simulation results demonstrate that the proposed algorithms achieve faster convergence rate and better noise reduction performance compared with other investigated algorithms. Moreover, the EGMACFxLMS algorithm exhibits the best noise reduction performance in high impulse noise input. [ABSTRACT FROM AUTHOR]

The conflict of filtered-x least mean square (FxLMS) algorithm between convergence speed and steady-state misalignment restricts the performance of the adaptive system. The convex combination approach can successfully avoid this contradiction as it combines two adaptive filters that adopt different step sizes. Firstly, the generalized versoria function is applied as the cost function to develop the filtered-x maximum versoria criterion (FxMVC) algorithm, which has good performance in the face of impulsive noise and performs better than the FxLMS algorithm. Then, inspired by the convex combination approach, C-FxMVC is developed by combining the FxLMS algorithm with the FxMVC algorithm to obtain better behavior, and the FxLMS algorithm is employed in the fast filter. To further optimize the performance of the presented algorithm, we use the method of amplitude-constraint, and the amplitude of e (n) and X (n) are restrained nicely. Numerous simulations are employed to verify the effectiveness and rationality of the presented algorithm, where the impulsive noise required for the simulation environment is consistent with symmetric α -stable (S α S) distribution model. Simulation results demonstrate that the proposed C-FxMVC algorithm can possess better efficacy in terms of convergence speed and noise reduction. [ABSTRACT FROM AUTHOR]