Secondary Path Modeling Research Articles

Enhanced selective delayless subband algorithm independent of primary disturbance configuration for multi-channel active noise control system in vehicles

The selective delayless subband structure stands out as a promising algorithmic choice for the multi-channel active control of vehicle interior noise, particularly in the context of road noise. This type of algorithm reduces the eigenvalue spread of the autocorrelation matrix of the signal by decomposing the signal into subbands, and the desired subbands are activated selectively, thus achieving a significant performance improvement while consuming less computational resources compared with the traditional fullband algorithms. Nevertheless, the effectiveness of the subband algorithm appears to be closely tied to the configuration of the primary disturbance signal. In cases where the primary disturbance signal encompasses both broadband and tonal components, the performance advantage of the subband algorithm becomes constrained. In this paper, we conduct a thorough comparative analysis of two extensively employed subband algorithms, namely the Morgan and the Milani methods, through simulations employing data obtained from a multi-channel active noise control (ANC) headrest system. The results indicate that the Morgan method outperforms the Milani method when configured optimally. Subsequently, we propose an enhanced version of the subband algorithm based on the Morgan method. The enhanced algorithm incorporates an additional sinusoidal noise canceller (SNC) subsystem and a narrowband active noise control (NANC) subsystem based on local secondary path modeling to address tonal components, while the selective subband structure is employed for controlling broadband components. In addition, the computational complexity of the algorithm is analyzed. The effectiveness of the proposed algorithm is validated through numerous simulations and the ANC tests conducted on an actual vehicle utilizing the multi-channel ANC headrest system. The performance of the proposed algorithm surpasses that of traditional selective subband and fullband algorithms, and balanced binaural noise reduction is achieved.

Multi-channel ANC System with Online Secondary Path Modeling for Turboprop Aircraft Cabin

Multi-channel ANC System with Online Secondary Path Modeling for Turboprop Aircraft Cabin

A Modified Simultaneous Perturbation Stochastic Approximation Algorithm for Active Noise Control

Secondary path modeling is an important step in active noise control. Keeping the active noise control system stable, when the secondary path changes over time, is a widely studied problem. Using the simultaneous perturbation stochastic approximation algorithm, can update the control filter without secondary path modeling, avoiding the problem of increased noise caused by secondary path modeling. In this paper a variant of the simultaneous perturbation stochastic approximation algorithm is proposed, which incorporates historical coefficients of the control filter, to correct the direction of the gradient calculation. Based on an experimental setup with a realistic transfer path, this paper demonstrates that the proposed algorithm has superior performance in terms of convergence speed and noise reduction amount, than the general simultaneous perturbation stochastic approximation algorithm.

A Computation-efficient Online Secondary Path Modeling Technique for Modified FXLMS Algorithm

This paper proposes an online secondary path modelling (SPM) technique to improve the performance of the modified filtered reference Least Mean Square (FXLMS) algorithm. It can effectively respond to a time-varying secondary path, which refers to the path from a secondary source to an error sensor. Unlike traditional methods, the proposed approach switches modes between adaptive ANC and online SPM, eliminating the use of destabilizing components such as auxiliary white noise or additional filters, which can negatively impact the complexity, stability, and noise reduction performance of the ANC system. The system operates in adaptive ANC mode until divergence is detected due to secondary path changes. At this moment, it switches to SPM mode until the path is remodeled and then returns to ANC mode. Furthermore, numerical simulations in the paper demonstrate that the proposed online technique effectively copes with the secondary path variations.

A robust auxiliary noise power scheduling strategy for online secondary path modeling in active noise control systems

Auxiliary noise injected in active noise control (ANC) systems for online secondary path modeling (SPM) contributes to the residual noise, and thus deteriorates the noise control performance of ANC systems. Moreover, sudden changes in secondary path may lead to easy divergence of the existing online SPM methods. This paper improves the existing methods from three aspects to further mitigate these problems: 1) the noise power is varied on the basis of the convergence status of SPM filter so that large-power noise is injected at the start or when secondary path changes and the power is reduced as the filter converges. 2) a novel ON/OFF controlling strategy is used in the method so that the noise injection is suspended at the optimum point when the modeling accuracy is sufficient. 3) continuous check for error outliers is applied in the method to detect changes in secondary path so that the injection of noise can be reactivated in the case of path perturbation. Comparative simulations show the effectiveness of the improved method both in convergence and noise reduction performance. In addition, the improved method is robust against sudden changes in secondary path.

Convex combination of online secondary path modeling and improved cuckoo search for active noise control

This paper proposes an active noise control (ANC) algorithm based on convex combination of the online secondary path modeling (OSPM) and improved cuckoo search (ICS) algorithms. The ICS algorithm does not request the secondary path model to optimize the control filter. Therefore, the ICS algorithm is theoretically not affected by a varying secondary path, but its convergence is very slow. In contrast, the OSPM algorithm achieves faster convergence but is vulnerable to a sudden change in the secondary path. The proposed ANC algorithm combines the merits of both the OSPM and ICS algorithms by convex combination. Simulation results show that the proposed ANC algorithm can achieve better performance than the conventional ANC algorithm especially when the secondary path changes drastically.

Simultaneous perturbation algorithm for active noise control with an asynchronous wireless microphone

This paper investigates the issues on the use of an asynchronous wireless microphone in an active noise control (ANC) system. In a previous work, the jitter effect of the wireless error microphone is addressed by adopting the simultaneous variable perturbation (SVP) algorithm to update the control filter of the ANC system. However, another critical issue has been overlooked. The wireless microphone can have a different sampling rate from that of the ANC controller, because the analog-to-digital convertors for the ANC controller and the wireless microphone are triggered by separate crystal oscillators. This may deteriorate the convergence performance of the ANC system. In this paper, the simulation results demonstrate that both the SVP and online secondary path modeling (OSPM) algorithms are not affected by the sampling rate of the wireless error microphone, when the sampling rate difference is less than 0.01%. However, the sampling rate difference of the wireless reference microphone can lead to about 7 dB and 5 dB degradations in the noise reduction performance of the SVP and OSPM algorithms, respectively.

Frequency domain online secondary path modelling for active noise control without auxiliary noise

The identification of the secondary path transfer function is of vital importance for active noise control (ANC) system. Online secondary path modeling method is preferable to offline modeling due to its tracking capability in time-varying acoustic environments. However, the auxiliary noise of online modeling mixed with the residual error signal diminishes the noise control performance. In this paper, we propose a frequency domain online secondary path modelling algorithm based on our previous work which achieves simultaneous acoustic paths modeling and noise control without auxiliary noise. The uniqueness of the solution is theoretically analyzed, and the computational burden is shown significantly lower than the previously proposed method. Simulations are conducted to verify that the proposed algorithm also has a better convergence behavior.

A robust feedforward hybrid active noise control system with online secondary‐path modelling

In this study, a robust feedforward hybrid active noise control (ANC) system with online secondary-path modelling (SPM) is proposed that is capable of not only effectively suppressing the broadband and narrowband noise components but also tracking the secondary path (SP) variations. An finite impulse response online SPM subsystem as well as an efficient decoupling filter are included in the proposed feedforward hybrid ANC (HANC) system. The decoupling filter is a parallel-form bandpass filter bank that consists of multiple bandpass filters that are derived from the second-order infinite impulse response notch filters. It takes the residual noise as its input and separates the broadband component from the narrowband component, with the former used as not only a desired signal for the SPM but also as an error signal for updating the broadband sub-controller, whereas the latter adapted simultaneously to scale the auxiliary white Gaussian noise and to update the narrowband sub-controller. Extensive simulations are conducted with both the synthetic and real SPs as well as the synthetic and real noise signals that are generated by a large-scale factory cutting machine (strand-cutter) to demonstrate the advantages and effectiveness of the proposed feedforward HANC system. Comparisons are also demonstrated with the original HANC system as well as its directly extended version with an online SPM subsystem.

A simultaneous perturbation stochastic approximation algorithm for broadband noise control.

Simultaneous perturbation stochastic approximation (SPSA) algorithm, an algorithm without secondary path modeling, has been applied to active noise control by some researchers. Some extended versions of this algorithm have been also developed to improve its performance. However, these existing algorithms are mostly dedicated to controlling the periodic noise instead of the broadband noise. In particular, background noise is not taken into account when SPSA algorithms are applied to control broadband noise. In this paper, an algorithm combining the cost function with the SPSA algorithm to control broadband noise has been proposed. The suggested cost function is an inner product of the estimated cross-correlation function between a reference vector and the error signal. The elements of the reference vector are composed of the reference signals at different times. Moreover, the algorithm analysis is performed and the numerical simulations are carried out to demonstrate the validity of the proposed algorithm. The results illustrate that the proposed algorithm can effectively reduce broadband noise when interference noise exists in the control system. Furthermore, the proposed algorithm has better convergence performance than other SPSA algorithms.

An online identification method for virtual sensing in ANC system

Active noise control (ANC) system generates anti-noise with the same amplitude and opposite phase to reduce unwanted acoustic noise based on acoustic wave superposition. In general, ANC can generate a zone of quiet (ZoQ) around the error microphone position. However, if the microphone cannot be placed at the desired position, the reduction effect may not be sufficient in the desired position. In such a case, we need to use the virtual sensing method to achieve noise reduction at the desired position without placing the physically microphone. In virtual sensing method, secondary path variation can significantly degrade performance, and secondary path modeling errors can cause the system to diverge. In this presentation, we introduce a new online identification method for auxiliary filter-based virtual sensing in ANC system. Virtual sensing with online identification requires re-estimation of not only the secondary path but also the auxiliary filters. The proposed online identification method has the capability of online identification of auxiliary filters in addition to the conventional online identification of secondary paths. We show that through computer simulations the noise reduction can be maintained by applying the proposed online identification method even when the secondary path varies.

Careful least squares active noise control with no prior secondary path model

SummaryIn feedforward active noise and vibration control (ANVC) or active noise control (ANC), with online secondary path modeling, fast converging overall modeling algorithms may give high modeling errors. These errors are due to the highly correlated inputs, namely the reference and antinoise signals. These high modeling errors can result in poor performance if ignored. However, it is possible to consider these modeling errors in the control filter design, resulting in a careful controller and good performance, as proposed in this work. Careful control is a dual controller feature, which is the solution to the optimal control problem, but this has not been proposed before in the context of ANC. The modeling error covariance matrix (Sigma(n)) is available in the Kalman filter. However, this requires the knowledge of the time‐varying measurement noise power q(n) and state noise. Instead, this work uses a different approach in which the corresponding linear system is solved in a non‐recursive manner. The resulting maximum error is used for a worst‐case estimate of q(n), and, as it turns out, this can be used to get a worst‐case estimate of Sigma(n). A lower control filter update frequency and other optimizations keep computational complexity similar to other approaches. The resulting algorithm has very few easily selected parameters, good performance, does not require any prior secondary path estimate, and adapts to slow and sudden changes in the physical paths without loss of performance or noise overshoots. However, performance can be impaired by large background noise levels.

An improved parallel narrowband active noise control system with local on-line secondary-path modeling equipped with frequency estimation algorithm

Active noise control technology shows good performance in those low-frequency narrowband noise control applications. Suffering from time-varying frequencies and secondary path, the performance of narrowband active noise control system may deteriorate seriously. In this work, an improved narrowband active noise control system is proposed to improve the noise reduction performance in those time-varying system. In the improved system, a frequency tracker based on the adaptive notch filter is adopted to estimate the frequencies of the noise signal. The result of frequency estimation is used to generate the reference signal and the prior information of the secondary-path modeling process. Then, the local secondary-path modeling can be conducted in the system with unknown fixed tonal noise signal or time-varying tones. Simulations and experiments are conducted to verify the performance of the proposed algorithm, and the results show that the proposed algorithm has good behavior.

Theoretical and experimental investigation study of discrete time rate‑dependent hysteresis modeling and adaptive vibration control for smart flexible beam with MFC actuators

A new rate‑dependent hysteresis modeling and identification method, and a new adaptive filtering control algorithm is proposed to solve the hysteresis nonlinearity identification problem and adaptive parameter auto-tuning problem for vibration control of smart flexible beam employing MFC actuators. A Hammerstein model is proposed by composing asymmetric Bouc-Wen model and ARX model in series to for secondary path modelling to model the MFC actuator hysteresis and mechanical hysteresis of the flexible beam. To improve the convergence speed and performance for complex parameter identification of the Hammerstein model, an improved Jaya algorithm named hybrid Jaya is proposed which combines the mutation strategy of two kinds of DE algorithms as the individual update method of the proposed hybrid Jaya. To balance the convergence speed and steady-state error for active vibration control performance, a new variable tap-length and step-size FXLMS algorithm named FX-VSSFT-LMS algorithm is proposed and the adaptive parameters are auto-tuned by our proposed Hybrid Jaya algorithm. To verify the proposed Hammerstein model and adaptive filtering algorithm, a cantilever beam experimental platform with MFC actuators is constructed. Hybrid Jaya algorithm and Jaya algorithm were used to identify the Hammerstein model and Bouc-wen model of MFC actuator, and the improved effects of hybrid Jaya algorithm and Hammerstein model were compared and verified. The FX-VSSFT-LMS algorithm is compared with FX-VSSLMS algorithm with variable step size and FX-FTLMS algorithm with variable tap-length on the MFC cantilever beam. The experimental comparison proves that the Hammerstein model identified by the proposed Hybrid Jaya algorithm is effective and the FX-VSSFT-LMS algorithm has better convergence performance than FX-VSSLMS and FX-FTLMS algorithm.

A Novel Narrowband Active Noise Control System with Online Secondary Path Modeling Based on Factor Decomposition and Application in Open Space

Due to the complexity of the coupling between the active noise control (ANC) controller and secondary path estimator, performance analysis of the system becomes particularly difficult. At present, the performance analysis of the system is often based on the fact that the secondary path tends to be stable, and the secondary path fitting error is minimal. However, in the early stage of system operation, or when the secondary path changes suddenly, the secondary path fitting error is significant, which easily causes divergence of the system control. It is still unable to guarantee the step-size bounds of convergence stability. Therefore, factor decomposition was used to analyze the mean weight behavior in this study. This strategy emphasizes the influence of secondary path modeling (SPM) error. The mean square behavior was evaluated using the energy conservation relationship. According to the established theoretical model, the convergence condition of the system was derived and the upper bound of step size suitable for all stages of system operation was obtained. The simulation and experimental results show that the ANC system is quite stable and robust under extreme conditions and has an obvious noise reduction effect in a specific range of open space, which can reach about 20 dB noise reduction.

An Improved Artificial Bee Colony Algorithm for Model-Free Active Noise Control: Algorithm and Implementation

Active noise control (ANC) usually adopts the filtered-x least mean square (FxLMS) algorithm. However, the FxLMS algorithm requires the identification of the secondary path. Problems of time-varying secondary path and imperfect secondary path modeling require model-free ANC algorithms for practical applications. In this paper, artificial bee colony (ABC) algorithm is improved to suitably develop a novel ANC algorithm without secondary path modeling. In addition, FxLMS algorithm may fall into local minima, while our proposed algorithm features its global optimization ability. In order to have anti-interference ability, in our algorithm, a forgetting factor is introduced into the fitness function. Moreover, the least mean square (LMS) algorithm is integrated into the ABC algorithm to enhance its exploitation ability, further accelerate the convergence rate and improve the noise reduction performance. Compared with some closely related population-based model free ANC algorithms, our proposed algorithm has anti-interference ability, faster convergence rate, and better noise reduction performance. Simulations and experiments are conducted to illustrate the effectiveness of the proposed algorithm.

A new online secondary path modeling method with white noise amplitude adjustment for excavator cab

This article describes a new active noise control system to reduce the noise in the excavator cab. The noise in the excavator cab is mainly low-frequency noise. A digital signal processor (DSP) is selected to control the microphone and speaker for active noise control. In the traditional online secondary path modeling method, the amplitude of the white noise is fixed, and the amplitude of the white noise is still large after the modeling is completed, which will affect the reference microphone. In response to the above problems, I made improvements in the traditional algorithm and added a white noise amplitude adjustment module. After the iterative algorithm of the identification filter converges, the amplitude of the white noise is reduced. In addition, when the secondary path is disturbed, the amplitude of the white noise is increased in order to remodel the secondary path. That is to say, the white noise amplitude adjustment is based on the first-order forward difference of the square value of the error signal every 50 times. What’s more, the iterative algorithm of the control filter and the identification filter adopts the idea of variable step size, which can speed up the convergence of the algorithm. Finally, the feasibility of the algorithm is verified through matlab simulation and experiment. In the simulation, it can achieve a noise reduction of about 11dB. In the experiment, a noise reduction of 5dB can be achieved, and a noise reduction of 9dB can be achieved at the frequency with the largest noise amplitude. In a word, the proposed algorithm is superior to traditional online secondary path modeling algorithm.

A Robust Feedback Active Noise Control System With Online Secondary-Path Modeling

In this letter, a robust feedback active noise control (FBANC) system with an online secondary-path modeling (OSPM) subsystem is proposed for suppressing sinusoidal noise signals. First, a decoupling scheme is proposed by using of an adaptive linear prediction filter (LPF) that takes the FBANC residual noise as its input and separates the remaining sinusoidal noise from the broadband noise that includes an inherent additive noise within the primary noise as well as a broadband component due to an auxiliary white Gaussian noise (AWGN). The remaining sinusoidal noise (LPF output) is used effectively not only to update the FIR controller but also to scale the AWGN, while the broadband noise (LPF error) serves as a desired signal for the OSPM. In this way, the hostile coupling between the FIR controller and the OSPM subsystem is significantly mitigated, leading to both faster system convergence and improved noise reduction performance (NRP). Second, a simple and practical scheme for refreshing the proposed FBANC system is also developed to enhance its robustness against even abrupt changes with the SP and/or the primary noise signal. Extensive simulations with synthetic and real secondary paths and noise signals are conducted to demonstrate the effectiveness of the proposed FBANC system.

A Noise Control Method Using Adaptive Adjustable Parametric Array Loudspeaker to Eliminate Environmental Noise in Real Time.

Long-term exposure to environmental noise is dangerous to human health. Therefore, there is an urgent need to suppress or eliminate environmental noise. Due to the limitation of environmental space, the use of reverse sound waves emitted by loudspeakers for noise elimination has been widely used in noise control. However, because of the omni-directionality of sound propagation, a traditional voice coil loudspeaker (VCL) is used as a secondary source (emission reverse sound wave). It is easy to increase the sound pressure in non-target areas and form significant acoustic feedback to the reference source. Therefore, we propose an online secondary path modeling method using an adjustable parametric array loudspeaker (PAL) based on ultrasounds to eliminate environmental noise in real time. According to the different distance of the target, the size of the PAL is adjusted adaptively to realize the noise control of different long-distance targets. The distribution of quiet areas is discussed. The experimental results showed that a PAL as a secondary source had the same noise reduction effect as a traditional VCL, but it had longer propagation distance, smaller sound feedback and a more regular and controllable distribution of quiet areas. These research findings have great potential for improving environmental noise and creating a quiet environment.

Active noise control without secondary path modeling: algorithm and implementation

Active noise control (ANC) has been intensively studied for decades. The most classical ANC algorithm should be the filtered-x least mean square (FxLMS) algorithm, which needs the model of the secondary path to work. Thus, the residual error of the ANC system is closely related to the preciseness of the secondary path model. In many applications, the secondary path is often time-varying. Therefore, off-line identification of the secondary path is not applicable. However, on-line identification often requires an additional white noise as a stimulating signal of the secondary path, which will deteriorate the final noise reduction effect. This paper proposes an improved artificial bee colony (ABC) algorithm for ANC system, which does not require identification of the secondary path. In order to guarantee the convergence of the algorithm and accelerate the convergence speed, this paper introduces a variable forgetting factor into the fitness function, and improves the traditional ABC algorithm by integrating LMS algorithm into the ABC algorithm. A single channel ANC system equipped with an FPGA hardware platform is set up in an anechoic chamber, and experiments show that the proposed algorithm can produce a satisfactory noise reduction effect without modeling the secondary path.