A multi-channel active noise control system using deep learning-based method to estimate secondary path and normalized-clustered control strategy for vehicle interior engine noise

Although the multi-channel active noise control (ANC) system based on the traditional clustered control strategy solves the problems of high algorithm complexity and fragile stability, the step size setting of each local controller relies on the trial-and-error method, which makes it difficult to trade-off the convergence speed and the steady-state error of the algorithm, and the secondary path estimation is susceptible to the interference of the dynamic environment. To solve the above problems, this study proposes an efficient multi-channel ANC system, which accurately estimates the secondary paths based on the deep learning prediction model and adopts a normalized-clustered control strategy to normalize the step size of the two-channel FxLMS algorithm of each local controller, which balances the system convergence speed and the steady-state error. A series of real-vehicle ANC experiments are conducted. The results show that under stationary conditions, the proposed control strategy control converges quickly and has better noise reduction performance than the traditional clustered control strategy. Under non-stationary conditions, after normalizing the step size of the local controller, the proposed control strategy can better balance the steady-state error and the convergence speed of the control strategy and improve the noise reduction tracking ability. Finally, it is verified that the proposed deep learning method can accurately estimate the secondary path after changes and ensure the noise reduction performance of the proposed control strategy.