The MMFxLMS algorithm for active noise control with on-line secondary path modelling

Lopes, Paulo A. C.; Gerald, José A. B.; Piedade, M.S.

doi:10.1016/j.dsp.2016.08.012

Cited by 20 publications

(16 citation statements)

References 15 publications

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“…Other algorithms do not perform as well under these conditions. 2,13 Figure 5 shows a spectrogram with one of the simulations. It can be seen that, in this case, the narrowband noise is quickly removed after ANC is turned on at 1 s. The large and sudden change of the SP at 5 s is visible but is also quickly compensated.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…This work builds on the work presented in the work of Lopes et al, 2 whereby MMFxLMS is modified for narrowband ANC and combined with MFxLMS for narrowband ANC to form the M 3 FxLMS.…”

Section: Fxlms Algorithmmentioning

confidence: 99%

“…The resulting algorithm is presented in Table 1. Note that there are some changes in Table 1 when compared with the algorithm proposed in the work of Lopes et al 2 The step sizes used in the calculations ofp andŝ are ′ p = sp ∕2∕[x H (n)x(n) + q(n)] and ′ s = sp ∕2∕[y H (n)y(n) + q(n)], as seen in (23) and (26), rather than ′ p = ′ s = sp ∕[x H (n)x(n) + y H (n)y(n) + q(n)]. This improves the convergence properties of the algorithm by reducing the eigenvalue spread of the autocorrelation matrix.…”

Section: Fxlms Algorithmmentioning

confidence: 99%

“…where in the expectation, it is assumed that ||x(n)|| 2 and ||y(n)|| 2 are known and v v (n) = E[v 2 (n)]. Taking into account the values of a 2 (n) and b 2…”

Section: Analysis Of the Mmfxlms Algorithm For Narrowband Signalsmentioning

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: Simulation Resultsmentioning

confidence: 99%

“…This work builds on the work presented in the work of Lopes et al, 2 whereby MMFxLMS is modified for narrowband ANC and combined with MFxLMS for narrowband ANC to form the M 3 FxLMS.…”

Section: Fxlms Algorithmmentioning

confidence: 99%

Section: Fxlms Algorithmmentioning

confidence: 99%

“…where in the expectation, it is assumed that ||x(n)|| 2 and ||y(n)|| 2 are known and v v (n) = E[v 2 (n)]. Taking into account the values of a 2 (n) and b 2…”

Section: Analysis Of the Mmfxlms Algorithm For Narrowband Signalsmentioning

confidence: 99%

See 2 more Smart Citations

A narrowband active noise control system with reference synthesis

Lopes

Gerald

2019

Adaptive Control & Signal

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“…In the aforementioned improved methods, the authors have proposed their own ANPS method based on their respective theories. There are also other studies in the literature about ANPS for the active noise and vibration control systems (Ahmed et al, 2013; Lopes et al, 2015, 2016; Pu et al, 2014b). In those studies, the main idea of the authors is to inject large-power auxiliary noise at the start up or when a change occurs in the secondary path, and to reduce the auxiliary noise power as the online SPM filter converges.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of multichannel adaptive active vibration control with online secondary path modeling using piezoelectric transducers patches

Yang

Meng

Zhou

2018

Transactions of the Institute of Measurement and Control

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Adaptive active vibration control (AAVC) is an effective way for reducing structure vibration at low frequency. AAVC methodology is preferred in AVC system due to its self-adjustment ability to adapt to varying dynamics of the structure. The Filtered-X Least Mean Square (FXLMS) algorithm is widely implemented in active control applications. Accurate secondary path models are very crucial for the implementation in multichannel AAVC system based on FXLMS algorithm. The auxiliary random noise technique is often applied to achieve secondary path modeling (SPM) during online operation. This paper proposes a new multichannel AAVC methodology based on online SPM method. The online SPM error is estimated indirectly to reduce the interaction between the AAVC controller and the online SPM filter. A new variable step-size (VSS) strategy for online SPM filter is proposed based on the estimated online SPM error. A simple but effective auxiliary noise power scheduling (ANPS) method is proposed to eliminate the contribution of the auxiliary noise on the residual vibration. A series of multi-channel AAVC experiments on a cantilever epoxy resin plate with PZT sensors and actuators are presented to demonstrate the performance of the proposed methodology. Experiment results indicate that the proposed method provides very good online SPM accuracy, and the vibration of the smart cantilever plate has been effectively attenuated with high convergence rate.

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Frequency domain analysis of the mirror‐modified filtered‐x least mean squares algorithm with low ambient noise

Lopes

Gerald

2021

Adaptive Control & Signal

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Summary The mirror‐modified filtered‐x least mean squares (MMFxLMS ) algorithm is a variation of the FxLMS algorithm with online secondary path modeling that cannot diverge due to secondary path modeling errors. However, problems may occur when the ambient noise is not limited due to insufficient modeling power. This work shows that under a frequency domain analysis without ambient noise, the MMFxLMS algorithm is always stable, and expressions for the maximum residual noise level at any given time are obtained. It is also shown that, under the same context, convergence to the minimum residual noise is guaranteed. Still, convergence can be much slower for high secondary path modeling errors than that of the LMS or MFxLMS algorithms. Simulations confirm these results.

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The MMFxLMS algorithm for active noise control with on-line secondary path modelling

Cited by 20 publications

References 15 publications

A narrowband active noise control system with reference synthesis

A narrowband active noise control system with reference synthesis

Design and implementation of multichannel adaptive active vibration control with online secondary path modeling using piezoelectric transducers patches

Frequency domain analysis of the mirror‐modified filtered‐x least mean squares algorithm with low ambient noise

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