We propose online feedback path modeling with a pre-inverse type active noise control (PIANC) system to track the fluctuation stably in the feedback path. The conventional active noise control (ANC) system with online feedback path modeling (FBPM) filter bases filtered-x least mean square (FxLMS) algorithm. In the FxLMS algorithm, the error of FBPM influences a control filter, which generates an anti-noise, and secondary path modeling (SPM) filter. The control filter diverges when the error is too large. Therefore, it is difficult for the FxLMS algorithm to track the feedback path without divergence. On the other hand, the proposed approach converges stably because the FBPM filter's error does not influence a control filter on the PIANC system. Thus, the proposed method can reduce noise while tracking the feedback path. This paper verified the effectiveness of the proposed method by convergence analysis, computer simulation, and implementation of a digital signal processor.

We propose an auxiliary-noise power-scheduling method for a pre-inverse active noise control (PIANC) system. Conventional methods cannot reduce the power of auxiliary-noise due to the use of the filtered-x least mean square (FxLMS) algorithm. We developed our power-scheduling method for a PIANC system to solve this problem. Since a PIANC system uses a delayed input signal for a control filter, the proposed method delivers stability even if the acoustic path fluctuates. The proposed method also controls the gain of the auxiliary-noise based on the secondary-path-modeling state. The proposed method determines this state by the variation in the power of the secondary-path-modeling-error signal. Thus, the proposed method changes the power-scheduling of the auxiliary-noise. When the adaptive algorithm does not sufficiently converge, the proposed method injects auxiliary-noise. However, auxiliary-noise stops when the adaptive algorithm sufficiently converges. Therefore, the proposed method improves noise reduction performance.

Based on the theoretical analysis of literature, saturation in measured signal of active noise control (ANC) systems will degrade the convergence speed. However, the experiments show that the saturated input signal can speed up the convergence of the narrow-band ANC systems. This paper intends to remodel the saturation effects for feedforward and feedback ANC systems. Combining the action of analog-to-digital converters (ADC), the mathematical expression and block diagrams are proposed to model the saturation effects in the practical ANC systems. The derivation and simulation results show that since the saturation is able to amplify the principle component of signal, the convergence would be speeded up.