Online secondary path modeling method with auxiliary noise power scheduling strategy for multi-channel adaptive active noise control system

Yang, Pu; Shu, Peng-Fei

doi:10.1177/1461348419831119

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…The following equations describe the detailed calculation process of x ( n ) and y ( n ).

where

are the coefficients of the M th order FIR filter

used to estimate the secondary path, which can be determined either offline or online [ 32 ],

are the coefficients of the adaptive FIR filter W ( z ) at time n , and L is the order of W ( z ). These coefficients are updated by the FxLMS algorithm as

where μ is the step size, and the filtered reference signal x ′( n ) is

…”

Section: Feedback Anc Based On a Single Microphone Sensormentioning

confidence: 99%

A Feedback Active Control Approach to Road Noise Based on a Single Microphone Sensor to Improve Automotive Cabin Sound Comfort

Liu,

Lee

2024

Sensors

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Tire–road noise deteriorates the sound quality of a vehicle’s interior and affects the driving safety and comfort. Obtaining low interior noise is a challenge for passenger car manufacturers. Traditional passive noise control (PNC) is efficient for canceling high frequency noise but not useful for low frequency noise, while active noise control (ANC), according to the residual error signal, can generate an anti-noise signal to reduce the original noise. Most research has focused on improving the control effect for a feedforward ANC system. However, this paper emphasizes a feedback ANC system based on a signal microphone sensor. There are two main contributions in this study to improve automotive cabin sound comfort. One is that the algorithm of the feedback ANC system using a single microphone sensor without a reference noise signal is proposed based on the Filtered-x Least Mean Square method. The other is that the algorithm applies additive random noise online to estimate the secondary path model. A simulation was implemented based on measured real road noise data, and the simulation results indicate that the proposed feedback ANC system with the single microphone sensor can effectively attenuate road noise. This study shows the feasibility of applying a feedback ANC system in automobiles to increase the cabin sound quality.

show abstract

“…The following equations describe the detailed calculation process of x ( n ) and y ( n ).

where

are the coefficients of the M th order FIR filter

used to estimate the secondary path, which can be determined either offline or online [ 32 ],

are the coefficients of the adaptive FIR filter W ( z ) at time n , and L is the order of W ( z ). These coefficients are updated by the FxLMS algorithm as

where μ is the step size, and the filtered reference signal x ′( n ) is

…”

Section: Feedback Anc Based On a Single Microphone Sensormentioning

confidence: 99%

A Feedback Active Control Approach to Road Noise Based on a Single Microphone Sensor to Improve Automotive Cabin Sound Comfort

Liu,

Lee

2024

Sensors

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“…which can be obtained online (Padhi et al, 2018;Pu and Shu, 2019;Tahir, 2021) or offline by additional system identification.…”

Section: System Constructionmentioning

confidence: 99%

“…P (z) is the transfer function of primary path model P (n) and S (z) is the transfer function of secondary path model S (n). Here, for convenience of further derivation, we take S^(z) as an accurate estimate of S (z), namely, assuming thatwhich can be obtained online (Padhi et al, 2018; Pu and Shu, 2019; Tahir, 2021) or offline by additional system identification.…”

Section: Self-balancing Collaboration Controllermentioning

confidence: 99%

Self-balancing collaboration between linear and nonlinear adaptive sub-filters for hybrid active noise control

Zhou

Jia

et al. 2022

Journal of Vibration and Control

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Conventional adaptive filters for active noise control (ANC) are troubled by the trade-off between convergence speed and steady state error, especially when nonlinearity plays an important role in system model. In this paper, a hybrid ANC algorithm called FBFLANN is constructed by the collaboration of FIR and bilinear functional link artificial neural network. The characteristics of linear and nonlinear sub-filters are analyzed and utilized to facilitate the adaptability in noise reduction. A convex factor is employed to automatically balance the contribution of two sub-filters, achieving faster convergence and higher accuracy in the presence of nonlinear distortions. The mathematical working and updating rules of the controller are derived to regulate the ANC process theoretically. The effectiveness of the proposed FBFLANN algorithm is demonstrated by numerous simulation studies considering diverse nonlinear acoustic path models as well as acoustic feedback interference. Hardware ANC platform is built up to verify the real-time noise control performance in physical noisy environments. The simulation and experiment results confirm the applicability and prospects for further development of the presented architecture in noise control scenarios.

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“…This work was improved with an additional filter to reduce the mutual interference between secondary path modeling, and control 14‐16 . Auxiliary noise power scheduling was introduced in Reference 17 and improved in References 18‐24. However, most online secondary path modeling algorithms were designed to deal with slow secondary path changes and can diverge if the initial estimation error is too large or with sudden changes 8,22 .…”

Section: Introductionmentioning

confidence: 99%

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

Lopes

Gerald

2022

Adaptive Control & Signal

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In 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.

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Online secondary path modeling method with auxiliary noise power scheduling strategy for multi-channel adaptive active noise control system

Cited by 10 publications

References 16 publications

A Feedback Active Control Approach to Road Noise Based on a Single Microphone Sensor to Improve Automotive Cabin Sound Comfort

A Feedback Active Control Approach to Road Noise Based on a Single Microphone Sensor to Improve Automotive Cabin Sound Comfort

Self-balancing collaboration between linear and nonlinear adaptive sub-filters for hybrid active noise control

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

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