Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/169089
Title: MOV-modified-FxLMS algorithm with variable penalty factor in a practical power output constrained active control system
Authors: Lai, Chung Kwan
Shi, Dongyuan
Lam, Bhan
Gan, Woon-Seng 
Keywords: Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2023
Source: Lai, C. K., Shi, D., Lam, B. & Gan, W. (2023). MOV-modified-FxLMS algorithm with variable penalty factor in a practical power output constrained active control system. IEEE Signal Processing Letters, 30, 723-727. https://dx.doi.org/10.1109/LSP.2023.3286808
Journal: IEEE Signal Processing Letters 
Abstract: Practical Active Noise Control (ANC) systems typically require a restriction in their maximum output power, to prevent overdriving the loudspeaker and causing system instability. Recently, the minimum output variance filtered-reference least mean square (MOV-FxLMS) algorithm was shown to have optimal control under output constraint with an analytically formulated penalty factor, but it needs offline knowledge of disturbance power and secondary path gain. The constant penalty factor in MOV-FxLMS is also susceptible to variations in disturbance power that could cause output power constraint violations. This letter presents a new variable penalty factor that utilizes the estimated disturbance in the established Modified-FxLMS (MFxLMS) algorithm, resulting in a computationally efficient MOV-MFxLMS algorithm that can adapt to changes in disturbance levels in real-time. Numerical simulation with real noise and plant response showed that the variable penalty factor always manages to meet its maximum power output constraint despite sudden changes in disturbance power, whereas the fixed penalty factor has suffered from a constraint mismatch.
URI: https://hdl.handle.net/10356/169089
ISSN: 1070-9908
DOI: 10.1109/LSP.2023.3286808
Schools: School of Electrical and Electronic Engineering 
Rights: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/LSP.2023.3286808.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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