New FIR filter-based adaptive algorithms incorporating with commutation error to improve active noise control performance

Liao, Ching-Wen; Lin, Jing‐Jenn

doi:10.1016/j.automatica.2006.08.019

Cited by 19 publications

(13 citation statements)

References 6 publications

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“…A normalized LMS (NLMS)-based algorithm to update coefficients of W .´/ to minimize " 2 1 .n/ can be derived as where˛> 0 is a step size. This algorithm is referred to as NFxLMS/CE algorithm [3]. The initial condition of (14) is generally given as w.n 0 / D 0, where n 0 denotes the initial time.…”

Section: Exponential-decay Step Size For Lms-based Anc Algorithmmentioning

confidence: 99%

“…ANC computer simulations were conducted for EDSS-NFxLMS/CE_DC algorithm according to the block diagram shown in Figure 2 where P 1 .´/ and P 2 .´/ were measured from a real experiment setup using an acoustic duct [3] at a sampling time of T s D 10 4 s and given in Table I. Let W .´/ …”

Section: Computer Simulationsmentioning

confidence: 99%

“…The error output e.n/ D d.n/ C y 0 .n/ was obtained and shown in Figure 3, indicating good noise attenuation. To show convergence rate and level of noise reduction, we adopted the method previously reported in [3] and used a 0.1-s (10 3 sampling time) window to compute a mean square value of e.n/ as…”

Section: Computer Simulationsmentioning

confidence: 99%

“…ANC applications commonly use the finite-impulse-response (FIR) filter-based filteredreference-signal least-mean-square (FxLMS) algorithm for its simplicity, but this approach suffers from slow convergence. An FxLMS algorithm with commutation error (CE) named FxLMS/CE algorithm is then obtained in [3] to overcome the slow-convergence problem and achieve better ANC performance. Like the FxLMS algorithm, the FxLMS/CE algorithm relies on perfect error sensor measurements.…”

Section: Introductionmentioning

confidence: 99%

“…This study aims to develop two techniques including exponential-decay step size (EDSS) and disturbance compensation (DC) for a normalized FxLMS/CE (NFxLMS/CE) algorithm [3] in ANC systems subject to disturbance such that the misalignment can reduce, resulting in a better ANC performance. The remainder of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Development and convergence analysis of new least‐mean‐square‐based algorithm equipped with exponential‐decay step size and disturbance compensation for active noise control

Lin

2012

Adaptive Control & Signal

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In real-world active noise control (ANC) applications, disturbance can be picked up by error sensors and significantly degrade the steady-state ANC performance. This study proposes two techniques in combination with a least-mean-square (LMS) based ANC algorithm, named normalized filtered-x LMS/commutation error (NFxLMS/CE) algorithm, to deal with the disturbance that is independent of a reference signal. A new stochastic method to analyze convergence properties of the NFxLMS/CE algorithm under influence of the disturbance is first established. Given that the reference signal is persistently exciting of sufficient order, exponential convergence of the algorithm is derived with a step-size condition. An exponential-decay step size (EDSS) is then proposed to obtain a new ANC algorithm referred to as EDSS-NFxLMS/CE algorithm. In addition, a disturbance-compensation (DC) technique is developed for the EDSS-NFxLMS/CE algorithm to obtain an EDSS-NFxLMS/CE_DC algorithm such that the influence of the disturbance can be reduced. It is shown that the EDSS-NFxLMS/CE_DC algorithm is exponentially convergent. Moreover, computer simulations show that the EDSS-NFxLMS/CE_DC algorithm can achieve a better ANC performance in terms of convergence rate and level of noise reduction as compared with that using the EDSS-NFxLMS/CE algorithm without DC and that using NFxLMS/CE_DC algorithm of constant step sizes. These results support the effectiveness of the proposed techniques and EDSS-NFxLMS/CE_DC algorithm. Copyright

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Section: Exponential-decay Step Size For Lms-based Anc Algorithmmentioning

confidence: 99%

Section: Computer Simulationsmentioning

confidence: 99%

Section: Computer Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development and convergence analysis of new least‐mean‐square‐based algorithm equipped with exponential‐decay step size and disturbance compensation for active noise control

Lin

2012

Adaptive Control & Signal

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A dual filtering scheme for nonlinear active noise control

Delvecchio

Piroddi

2013

Adaptive Control & Signal

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Active noise control problems are often affected by nonlinear effects such as distortion and saturation of measurement and actuation devices, which call for suitable nonlinear models and algorithms. The active noise control problem can be interpreted as an indirect model identification problem, due to the secondary path dynamics that follow the control filter block. This complicates the weight update mechanism in the nonlinear case, in that the error gradient depends on the secondary path gradient through nonlinear recursions. A simpler and computationally less demanding approach is here proposed that employs the updating scheme of the standard filtered-x least mean squares (LMS) or filtered-u LMS algorithm. As in those schemes, the calculation of the error gradient requires a signal filtering through an auxiliary system, here obtained through a secondary adaptation loop. The resulting dual filtering LMS algorithm performs the adaptation of the controller parameters in a direct identification mode and can therefore be easily coupled with adaptive model structure selection schemes to provide online tuning of the model structure, for improved model robustness.

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Bearing Fault Detection and Classification Using ANC-Based Filtered Vibration Signal

Sahoo

Das

2018

Lecture Notes in Electrical Engineering

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New FIR filter-based adaptive algorithms incorporating with commutation error to improve active noise control performance

Cited by 19 publications

References 6 publications

Development and convergence analysis of new least‐mean‐square‐based algorithm equipped with exponential‐decay step size and disturbance compensation for active noise control

Development and convergence analysis of new least‐mean‐square‐based algorithm equipped with exponential‐decay step size and disturbance compensation for active noise control

A dual filtering scheme for nonlinear active noise control

Bearing Fault Detection and Classification Using ANC-Based Filtered Vibration Signal

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