噪声有源自校正预测控制

有源噪声控制（ANC）的经典方法是采用有限脉冲响应（FIR）滤波器的Filter-X算法，该算法的一个主要缺点是次级声路径响应对控制滤波器参数自适应的收敛速度有较大影响。将预测控制方法应用到有源噪声控制领域，给出了一种参数在线自适应算法，该算法的收敛速度不受次级声路径响应的影响。仿真结果表明，给出的控制方法比传的Filter-X控制有更小的稳态误差，而且收敛速度更快。

Active Self-correction Predictive Noise Control

Classical digital ANC systems use adaptive filtering techniques,often based on the filter-x algorithm for finite impulse response(FIR)filter.The major drawback of this method is that the convergence rate of filter-x algorithm is limited by the dynamic response of the sec-ondary path.An active noise self-tuning model predictive control approach is derived.An on-line learning algorithm is proposed for adjusting the uncertain model parameters.The convergence rate of this algorithm is not influenced by the dynamic response of the secondary path.A simulation example shows that compared with the filter-x algorithm control,the active noise self-tuning model predictive control has smaller stable error and faster convergence rate.