Secondary path modeling for narrowband active noise control systems

Chang, Cheng-Yuan; Kuo, Sen M.; Huang, Chiung-Wei

doi:10.1016/j.apacoust.2017.10.026

Cited by 49 publications

(20 citation statements)

References 22 publications

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“…Auxiliary noise power scheduling was first proposed in the work of Zhang et al 10 In the work of Akhtar et al, 11 a variable step size is introduced that increases as ANC converges. 15 Chang et al 16,17 propose to decouple estimation of the SP at different frequencies for narrowband ANC. Other related strategies are proposed in the work of Ahmed et al 14 The simultaneous equation method is proposed in the work of Fujii and Ohga.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Other related strategies are proposed in the work of Ahmed et al 14 The simultaneous equation method is proposed in the work of Fujii and Ohga. 15 Chang et al 16,17 propose to decouple estimation of the SP at different frequencies for narrowband ANC. Finally, the work of Padhi et al 18 compare the performance of several hybrid ANC systems with online SP modeling.…”

Section: State-of-the-artmentioning

confidence: 99%

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A narrowband active noise control system with reference synthesis

Lopes

Gerald

2019

Adaptive Control & Signal

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Most active noise control (ANC) algorithms require a model of the secondary path (SP). If there are large changes in the SP, online SP modeling algorithms may not be able to follow the changes quickly enough to prevent divergence. In this work, the mirror-MFxLMS (MMFxLMS) is adapted to narrowband ANC, and the resulting algorithm is analyzed. The MMFxLMS is an algorithm similar to the MFxLMS and FxLMS but has online SP modeling; it is stable for any SP estimate and does not require auxiliary noise. In addition, a technique to alternate between MFxLMS and MMFxLMS is proposed, resulting in the M 3 FxLMS algorithm, which does not sacrifice performance and assures stability. However, the M 3 FxLMS requires a reference signal that is not available in feedback ANC.Feedback ANC is important in many practical cases, for instance, in ANC headphones. To solve this problem, a technique is proposed to synthesize a reference formed by a sum of sinusoids using measurements from the error sensor signal.This technique uses the fast Fourier transform to obtain estimates of the harmonic frequencies of the periodic noise components, and then refines and tracks these using a phase-locked loop (PLL). The resulting algorithm enables a stable ANC system, even under large and sudden changes in the SP. The proposed PLL is also new and has a lock-in range equal to the full band of the system. KEYWORDS active noise control (ANC), feedback, FxLMS, narrowband, PLL, stability INTRODUCTIONActive noise control (ANC) is a technique where waves (antinoise) with opposite phase to a noise signal are used to reduce the noise signal level. 1 In feedforward ANC, a signal called the reference signal is used to generate the antinoise signal, using additional information from a set of error sensors that indicates the residual noise level. In feedback ANC, the signal from a set of error sensors is used to generate the antinoise signal. In narrowband ANC, the noise to be canceled is assumed to be periodic or a sum of periodic signals. This paper addresses single-channel (one error sensor) narrowband feedback ANC, with applications, for instance, in ANC headphones.Most ANC algorithms require a secondary path (SP) model and can become unstable if that model is not sufficiently accurate. The SP model can be obtained before the start of noise canceling (offline) or during operation (online). If the SP changes significantly during noise cancelation, online SP modeling is required. However, even with online SP modeling, if there is a large and sudden change, the model may not be updated quickly enough and the algorithm may become unstable. To solve this problem, the authors have previously proposed the mirror-MFxLMS (MMFxLMS) algorithm. 2 1118

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Section: State-of-the-artmentioning

confidence: 99%

Section: State-of-the-artmentioning

confidence: 99%

A narrowband active noise control system with reference synthesis

Lopes

Gerald

2019

Adaptive Control & Signal

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“…Some other modified FxLMS algorithms have also been proposed for improvement in online identification in ANC systems. 19,20 Unfortunately, an additive noise must be introduced for online identification, which will seriously affect control precision. Recently, several ANC methods without secondary path modeling have been presented.…”

Section: Introductionmentioning

confidence: 99%

A fast vibration-level adjustment method for low-frequency vibration calibration based on modified filtered-x least mean square algorithm

Chen

2020

Measurement and Control

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Traditionally, successive approximation method is applied to the vibration-level adjustment of vibration calibration system, which leads to a time-consuming work for low-frequency vibration calibration. In this paper, a novel control method for low-frequency vibration calibration system is proposed based on adaptive filter. First, the problem of the traditional vibration-level adjustment for low-frequency signals is depicted. Then, an adaptive control algorithm is presented, in which the control input is composed of two weighted sinusoidal signals with a phase difference of 90°. The weighted vector is updated in real time using a modified filtered-x least mean square algorithm. Unlike filtered-x least mean square algorithm, the proposed modified filtered-x least mean square algorithm does not require a preidentification of the controlled system and has a reduced computational complexity. The convergence property of the proposed method is analyzed in detail. Finally, the proposed method is implemented on a low-frequency vibration calibration system. Experimental results show that the proposed modified filtered-x least mean square algorithm can significantly reduce the time of the vibration-level adjustment in low-frequency band.

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“…For parallel-form FxLMS algorithm, there are also some other analyses and improvements (Pasco et al, 2012; Yang et al, 2008), which are mostly through the improvement of the adaptive algorithm (Chang et al, 2018; Chen et al, 2012; Huang et al, 2015). However, the robustness of the algorithm in AVC application can mot be ignored.…”

Section: Introductionmentioning

confidence: 99%

Multi-frequency micro-vibration control with hybrid adaptive control algorithm

Fang

Zhu

et al. 2020

Transactions of the Institute of Measurement and Control

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As a popular adaptive algorithm for active vibration control (AVC), filtered-x least mean square (FxLMS) algorithm sometimes fails to suppress multiple narrowband disturbance. To control multi-frequency structure vibration, parallel-form FxLMS algorithm can be employed. However, the conventional parallel-form FxLMS does not perform well in the situation of disturbance frequency mismatch and wideband measurement noise. For this issue, the majority of the improvement approaches are based on adaptive frequency estimator. It increased the computational burden of the parallel-form FxLMS algorithm. Therefore, taking the advantage of feedback control, a hybrid adaptive control for AVC is proposed in this paper. And the “posterior” residual error is adopted for better robustness. The convergence analysis of the hybrid AVC algorithm is given. Co-simulations with ADAMS and SIMULINK are implemented to verify the effectiveness in micro-vibration control system. Some comparison experiments are implemented in a micro-vibration AVC experimental system for several typical disturbances. Experimental results confirm the feasibility and effectiveness of the proposed algorithm.

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Secondary path modeling for narrowband active noise control systems

Cited by 49 publications

References 22 publications

A narrowband active noise control system with reference synthesis

A narrowband active noise control system with reference synthesis

A fast vibration-level adjustment method for low-frequency vibration calibration based on modified filtered-x least mean square algorithm

Multi-frequency micro-vibration control with hybrid adaptive control algorithm

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