# Laboratory Test of a Vehicle Active Noise-Control System Based on an Adaptive Step Size Algorithm
## 1 Introduction
[13] used the finite element analysis method to conduct a modal analysis of the interior space and obtained the frequency response curves of the corresponding nodes in the ear. By establishing the adaptive neural network active noise control system Simulink model, the simulation experiment was conducted. The experimental results show that the neural network active control system has a good noise reduction effect in the frequency band of 0-50 Hz, and the average noise reduction is 4.3 dB. At 86 Hz, the maximum noise reduction is 9.8 dB, and the system can control the low-frequency noise generated under road excitation. In the process of vehicle driving, it is easy to encounter impulse noise, such as in the speed bump, when the instantaneous vibration noise of the car is suddenly increased. For impulse noise, since there is no bounded second moment, the traditional FxLMS algorithm is no longer suitable for impulse noise. Many researchers have proposed various improved adaptive algorithms for impulse noise. [PERSON] et al. [14] proposed a filtered x-minimum mean P-power algorithm (FxLMP), which is based on minimizing the fractional low-order moments existing in a stable distribution. The results show that the FxLMP algorithm, where \(p<\alpha\), has good robustness in terms of impulse noise. [PERSON] et al. [15] proposed a simplified FxLMS algorithm. If the amplitude of the reference signal is higher than a certain value set, the reference signal that is higher than this value is set to 0. Compared with FxLMS, this algorithm has better robustness, but the convergence rate is slow. [PERSON] et al. [16] proposed an improved normalized step size algorithm (MNFxLMS) as a solution to improve the performance of the FxLMP algorithm. [PERSON] et al. [17] proposed an improved FxLMM pulse noise control algorithm. The results show that the FxLMM algorithm and the improved FxLMM algorithm are more robust than the FxLMS algorithm in suppressing large amplitude pulse noise. [PERSON] et al. [18] proposed a universal step size normalization FxLMS algorithm and normalized the step size with a Gaussian distribution function. The algorithm requires neither parameter selection nor cost function selection. The simulation and experimental results show that the proposed scheme shows good performance for S\(\alpha\)S pulse noise attenuation. [PERSON] et al. [19] proposed a convex combination method with the F\(\times\)GMN algorithm for impulse noise. The simulation results show that, compared with other existing algorithms, the C-ExGMN algorithm can achieve better convergence speed and a greater noise reduction effect under different noise input conditions. [PERSON] et al. [20] proposed a new adaptive step size algorithm by adjusting the step size factor of reference signal X(n) and error signal e(n) and obtained a better coefficient via particle swarm optimization. The simulation results show that the algorithm has a fast convergence speed and strong stability. [PERSON] et al. [21] proposed an improved normalized step size EFatanLMS (NSSEFxatanLMS) algorithm, which adaptively adjusts the step size coefficient to an appropriate value by using a new time-varying normalized function. The simulation results show that the algorithm is effective against both Gaussian noise and impulse noise. [PERSON] et al. [22] proposed an adaptive algorithm combining self-normalization and the p-norm. The self-normalization method regenerates the filtered reference signal into a nonlinear function to transform the reference signal. The simulation and the experimental results show that the proposed self-P-normalization algorithm is more effective in suppressing the update weight of traditional algorithms. In view of the interior noise of a complex noise environment, this paper proposes an adaptive step size FxLMS algorithm; this algorithm works according to the reference signal change step length.