A Study of Engine Intake Noise Control Based on the Improved Filtered-x Least Mean Square Algorithm. (27th May 2022) - Lan, Chaofeng; Zhang, Lei; Lv, Shou; Jiang, Pengwei
Abstract : To reduce the intake noise of automobile engines, an active control system model of engine intake noise is established with the Filtered-x least mean square (FxLMS) algorithm. The offline identification method is adopted to identify the secondary path. The engine speed signal is used to construct the reference signal of a sound source to avoid interference of a secondary sound source to the reference signal. A variable-step algorithm is proposed, in which parameters are added to the normalized algorithm instead of the sinusoidal variable-step algorithm to adjust the amplitude range. This algorithm not only has the advantages of fast convergence speed and small steady-state error but also adapts to the characteristics of a time-varying reference signal and easy selection of parameters. In this paper, the noise of automobile engines under the New European Driving Cycle (NEDC) is studied and the proposed algorithm has faster convergence speed compared with the normalization algorithm, better adaptability to the change of the reference signal, and better stability compared with the sinusoidal variable step-size algorithm. The results show that the algorithm proposed can effectively reduce the intake noise of the engine at each speed and the noise reduction effect can reach 23.11 dB at a certain frequency. Meanwhile, the stability of the system is improved.