A variable step-size NFB-LMS algorithm for active vehicle interior noise control

The LMS algorithm has an inherent shortcoming that the convergence speed can not be increased simultaneously with reducing the steady-state error. For the existing variable step-size LMS algorithm the convergence rate is low and the accuracy of estimating steady-state residual error is poor. To avoid such disadvantages, an active control method of vehicle interior noise based on variable step-size NFB-LMS algorithm is presented in this paper. The traditional LMS algorithm, the variable step-size LMS algorithm based on arctangent function and the variable step-size NFB-LMS algorithm are respectively applied to the active control experiments of the measured vehicle interior noise for comparison. The results show that the convergence speed of the variable step-size NFB-LMS algorithm is increased by 70% and the steady-state error is reduced by more than 90%, compared with the other two algorithms. Therefore, the variable step-size NFB-LMS algorithm has high efficiency in processing the vehicle interior noise signals, and provides a new method for active control of vehicle interior noise.