Implementation of minimum output variance filtered reference least mean square algorithm with optimal time-varying penalty factor estimate to overcome output saturation

Abstract

The minimum output variance filtered reference least mean square (MOV-FxLMS) algorithm can effectively prevent the instability of active noise control (ANC) systems caused by the output saturation of the secondary source. The penalty factor, a critical parameter in MOV-FxLMS algorithm, is usually determined by trial and error, and its inaccurate estimate degrades algorithm's performance. Previous studies prove that estimating the optimal penalty factor requires prior knowledge of the disturbance. In practice, the penalty factor varies with the acoustic environment and primary source. Hence, this paper proposes an optimal time-varying penalty factor estimate method, which can track the variation of the disturbance and primary noise and assist the MOV-FxLMS algorithm in achieving the optimal control with output constraint. Moreover, the proposed algorithm also efficiently reduces computations and storage capacity requirements compared to other algorithms. The numerical simulation not only demonstrates that the proposed algorithm can react to noise variations but also reduces the influence of uncorrelated signals at the error sensor. Furthermore, the real-time experiment on a noise duct demonstrates the effectiveness of the proposed algorithm for the output saturation problem, exhibiting practical significance.