Open AccessA new online secondary path modeling method with white noise amplitude adjustment for excavator cab
Author(s) -
Hongyue Long,
Haidong Zhao,
Qiang Liu
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2183/1/012032
Subject(s) - excavator , noise (video) , noise reduction , gradient noise , white noise , active noise control , noise control , value noise , computer science , amplitude , microphone , acoustics , noise measurement , electronic engineering , control theory (sociology) , engineering , noise floor , telecommunications , physics , artificial intelligence , sound pressure , mechanical engineering , image (mathematics) , control (management) , quantum mechanics
This article describes a new active noise control system to reduce the noise in the excavator cab. The noise in the excavator cab is mainly low-frequency noise. A digital signal processor (DSP) is selected to control the microphone and speaker for active noise control. In the traditional online secondary path modeling method, the amplitude of the white noise is fixed, and the amplitude of the white noise is still large after the modeling is completed, which will affect the reference microphone. In response to the above problems, I made improvements in the traditional algorithm and added a white noise amplitude adjustment module. After the iterative algorithm of the identification filter converges, the amplitude of the white noise is reduced. In addition, when the secondary path is disturbed, the amplitude of the white noise is increased in order to remodel the secondary path. That is to say, the white noise amplitude adjustment is based on the first-order forward difference of the square value of the error signal every 50 times. What’s more, the iterative algorithm of the control filter and the identification filter adopts the idea of variable step size, which can speed up the convergence of the algorithm. Finally, the feasibility of the algorithm is verified through matlab simulation and experiment. In the simulation, it can achieve a noise reduction of about 11dB. In the experiment, a noise reduction of 5dB can be achieved, and a noise reduction of 9dB can be achieved at the frequency with the largest noise amplitude. In a word, the proposed algorithm is superior to traditional online secondary path modeling algorithm.