Experimental study on narrowband active noise control for rotors emulating AAM using adaptive Kalman filtering

Rotor-induced tonal noise is a major source of acoustic discomfort in Advanced Air Mobility (AAM) cabins, particularly under time-varying rotor speeds. Narrowband active noise control (ANC) is effective for mitigating such tonal components; however, its performance strongly depends on accurate and timely tracking of the blade passing frequency (BPF). This study proposes a narrowband ANC system incorporating an innovation-based adaptive Kalman filter (AKF) for real-time BPF estimation. The proposed system combines the AKF with a Momentum Filtered-x Least Mean Square (MFxLMS) controller, enabling robust frequency tracking under dynamic operating conditions through adaptive adjustment of process and measurement noise covariances. The performance of the proposed approach is experimentally evaluated using a two-bladed rotor under two representative operating scenarios: a trapezoidal rotor rotational speed profile with predictable transitions and a stochastic profile with stochastic speed fluctuations. The results show that Kalman filtering significantly improves ANC performance by suppressing reference signal jitter and preventing noise amplification in frequency bands adjacent to the target BPF. Under predictable operating conditions, a conventional Kalman filter with fixed noise covariance matrices performance is comparable to that of the AKF. In contrast, under stochastic and highly dynamic conditions, the AKF demonstrates superior robustness by reducing tracking delay and maintaining phase synchronization, leading to improved ANC stability. These findings indicate that, for rotor-based narrowband ANC, frequency mismatch caused by tracking lag is more detrimental to control stability than mismatch caused by measurement noise, highlighting the importance of estimator responsiveness in dynamic AAM environments.