Active control of vehicle powertrain noise using adaptive notch filter with inverse model LMS algorithm
by Ji Xu; Guohua Sun; Tao Feng; Mingfeng Li; Teik C. Lim
International Journal of Vehicle Noise and Vibration (IJVNV), Vol. 12, No. 4, 2016

Abstract: Conventional active noise control (ANC) systems are typically configured with the filtered-x least mean squares (FXLMS) algorithm or its modified versions. However, the traditional FXLMS algorithm often exhibits a frequency-dependent convergence behaviour, which leads to a poor tracking ability and unbalanced performance at individual harmonics. In this study, a novel adaptive notch filter with inverse model least means squares (ANF-IMLMS) algorithm is proposed as the basis for active control of vehicle powertrain noise. The proposed algorithm possesses the following two salient features as compared to the filtered-x LMS type algorithms: 1) rapid convergence speed; 2) good computational efficiency. The convergence speed and computational complexity of the proposed algorithm is analysed first. Then, the proposed ANC system for vehicle powertrain noise configured with the new algorithm is evaluated. The results show obvious enhancement in the convergence speed and noticeable noise reductions for each engine harmonic over a broader frequency range.

Online publication date: Wed, 25-Jan-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Vehicle Noise and Vibration (IJVNV):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com