职位(财务)
主动噪声控制
噪音(视频)
控制理论(社会学)
电动汽车
计算机科学
算法
降噪
控制(管理)
工程类
汽车工程
人工智能
物理
图像(数学)
经济
功率(物理)
量子力学
财务
作者
Enlai Zhang,Zhilong Peng,Chunlong Yi,Qian Chen,Jianming Zhuo
标识
DOI:10.1177/10775463241276970
摘要
This paper proposed an improved filtered-x least mean square (FxLMS) algorithm based on the fuzzy control rule of Takagi–Sugeon–Kang (TSK) to solve the drawback of slow convergence for standard FxLMS algorithm. The TSK-FxLMS is a control framework with two inputs and three outputs constructed by the error signal and its integral as input variables. To validate its effectiveness and applicability, in-vehicle active noise control (ANC) modelling and adaptive noise reduction at the driver’s position of an electric bus are thoroughly investigated. Firstly, the four noise signals for the different working conditions at 50 km/h, acceleration, coasting and braking are collected, and their spectral analyses are performed. Secondly, the effects of step size and fuzzy control parameters of ANC model on weight convergence and noise reduction effect are analysed and determined. Finally, the results of calculating and comparing the residual signals’ waveforms, frequency spectra, sound pressure levels and tracking performance indicate that the proposed TSK-FxLMS algorithm outperforms the standard FxLMS algorithm with faster convergence and better noise reduction effect.
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