Laboratory Test of a Vehicle Active Noise-Control System Based on an Adaptive Step Size Algorithm

Abstract

NVH (noise, vibration, and harshness) is a key factor affecting vehicle comfort. Compared with traditional sound absorption and isolation methods, active noise control (ANC) offers a significant advantage in solving the problem of low- and medium-frequency noise from road surfaces. However, the classic filtered-x least mean squares (FxLMS) algorithm is ineffective in terms of adapting to different road noises to ensure a stable noise reduction effect when facing the complex and changeable noise environment of moving vehicles. Therefore, an adaptive step size algorithm (ASSFxLMS) is proposed in this paper, which can adjust the step size according to the size of the reference signal to ensure the stability of the adaptive process. In order to improve the performance of the algorithm, a particle swarm optimization algorithm is also used to automatically adjust the parameters, so that the step size of the adaptive algorithm always maintains a relatively ideal size. The simulated pulse noise of standard SαS distribution was used as the reference signal for the simulation. The simulation results show that compared with other algorithms, the proposed algorithm under different degrees of pulse noise conditions, noise reduction stability, and noise reduction amplitude are improved. In order to further verify the feasibility of the algorithm in vehicle road noise reduction, this paper also conducted a hardware-in-the-loop noise reduction experiment in the laboratory, employing the road noise data collected by the real vehicle. Under different interior noise conditions, the proposed active noise-control system has a maximum noise reduction effect of 12 dB for low-frequency noise below 100 Hz.