Iterative learning approach to active noise control of highly autocorrelated signals with applications to machinery noise

Lasota, Adam; Meller, Michał

doi:10.1049/iet-spr.2020.0064

Cited by 6 publications

(1 citation statement)

References 35 publications

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“…Unfortunately, majority of the existing approaches are not suitable for embedded applications that require spectral factorization to be performed online in real time, such as the iterative learning active noise control [7]. A noteworthy exception to this rule is a spectral factorization scheme inspired by control theory proposed in [10].…”

Section: Introductionmentioning

confidence: 99%

A Control Theoretical Approach to Spectral Factorization is Unstable

Meller

Lasota

2021

Circuits Syst Signal Process

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Section: Introductionmentioning

confidence: 99%

A Control Theoretical Approach to Spectral Factorization is Unstable

Meller

Lasota

2021

Circuits Syst Signal Process

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Active Control of Highly Autocorrelated Machinery Noise in Multivariate Nonminimum Phase Systems

Meller

Lasota

2022

Circuits Syst Signal Process

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In this paper, a novel multivariate active noise control scheme, designed to attenuate disturbances with high autocorrelation characteristics and preserve background signals, is proposed. The algorithm belongs to the class of feedback controllers and, unlike the popular feedforward FX-LMS approach, does not require availability of a reference signal. The proposed approach draws its inspiration from the iterative learning control and repetitive mode control methods, and employs a modified inverse model learning law. The classical inverse model learning law is well known to offer fast convergence and high steady-state performance, provided that the secondary path is minimum phase and well known. The proposed modified inverse model learning law employs a spectral factorization trick, which allows one to use the method with nonminimum phase plants of arbitrary order. Moreover, our scheme includes a controller bandwidth limiting mechanism that can be used to tune the disturbance rejection bandwidth and to improve the closed-loop robustness to errors in the model of the secondary path. The algorithm’s behavior and performance are verified with computer simulations that demonstrate suppression of electrical transformer noise and include realistic models of the secondary path. The results show high-level selective attenuation and fast convergence.

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Research on Active Noise Control System Based on FBFULMS Algorithm

Yuan

et al. 2022

2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

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Iterative learning approach to active noise control of highly autocorrelated signals with applications to machinery noise

Cited by 6 publications

References 35 publications

A Control Theoretical Approach to Spectral Factorization is Unstable

A Control Theoretical Approach to Spectral Factorization is Unstable

Active Control of Highly Autocorrelated Machinery Noise in Multivariate Nonminimum Phase Systems

Research on Active Noise Control System Based on FBFULMS Algorithm

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