Parameter Analysis and Optimization of Road Noise Active Control System 2022-01-0313

The parameter setting has a great influence on the noise reduction performance of the road noise active control (RNC) system. This paper analyzes and optimizes the parameters of the RNC system. Firstly, the model of the RNC system is established based on the FxLMS algorithm. Based on this model, taking the maximum noise reduction as the evaluation index, the sensitivity analysis of convergence coefficient, filter order, and reference signal gain was carried out using the Sobol method with the data measured by a real vehicle on asphalt pavement at 40km/h. The results show that there is no significant interaction between the three parameters. Then, using the idea of orthogonal experiment, the simulation results of the control model are analyzed by taking the maximum noise reduction as the evaluation index. It is found that the convergence coefficient has the greatest effect on the maximum noise reduction, followed by the filter order, and the reference signal gain has the least effect. Thirdly, an interval is delimited by the position of the optimal level, that is, the optimal interval is obtained. Taking the maximum and average noise reduction at two error microphones as indexes, the NSGA-II algorithm is used to optimize the RNC system, and the optimal parameters are obtained. Finally, using four acceleration sensors and two microphones, the RNC system hardware in the loop test platform is built. The parameters before and after optimization were used respectively to conduct a real vehicle road test on the asphalt pavement at the speed of 40km/h, and it was found that the maximum noise reduction of the optimized system increased by 2dBA.