一种基于范数方法的次级路径在线建模有源噪声控制算法OA

To mitigate the mutual influence of residual signals between the controller module and the online modeling module,and to address the issue of excessive updates of the adaptive filter when the secondary path undergoes abrupt changes.A norm-based online modeling active noise control algorithm is proposed,which assesses convergence by monitoring the average energy of the residual signal and dynamically adjusts the modeling step size accordingly.Additionally,a threshold is set for the norm of the adaptive filter coefficients,when the norm of the adaptive filter exceeds this threshold,a norm-constrained method is employed to limit excessive updates of the filter,ensuring stability and preventing over-adjustment.Compared to existing online modeling active noise control algorithms,the proposed algorithm effectively reduces modeling error and improves noise reduction performance.At steady state,the modeling errors for single frequency,composite frequency,and band-limited white noise are-43 dB,-38 dB,and-39 dB,respectively,wachieving noise reduction leve of 32 dB,35 dB,and 25 dB.The single frequency,composite frequency,and narrowband white noise are used as the primary noise sources in the simulation.When there is sudden large change in the secondary path information,results show the proposed algorithm can maintain the stability of the active noise control system,enhance the convergence speed of the modeling error,and achieve superior noise reduction performance at steady state.