A number of active noise control applications involve the need to control multiple stationary frequencies, a single time-varying frequency, or some combination of the two. The most common control approach is typically based on some version of the filtered-x algorithm. For this algorithm, the convergence and tracking speed is dependent on the eigenvalues of the filtered-x autocorrelation matrix,...

Current active noise control (ANC) technology cannot yield a balanced performance over broad frequency range when applied to powertrain noise control. It is because most of these ANC systems are configured with the traditional filtered-x least mean squares (FXLMS) algorithm with an inherent limitation in the frequency-dependent convergence behavior. In particular, the phase delay of the seconda...

In this paper, we propose a computationally efficient Legendre Neural Network (LNN) for nonlinear Active Noise Cancellation (NANC). Update algorithms for NANC with linear secondary path (LSP) based on Filtered-x Least Mean Square (FXLMS), Filtered-e Least Mean Square(FELMS) and Recursive Least Square(RLS) are developed. Update algorithm for NANC with nonlinear secondary path(NSP) is also develo...

Adaptive filtering algorithms promise an improvement of the active noise control (ANC) problem encountered in many scenarios. Just to name a few, the Filtered-X Least Mean Square (FXLMS) algorithm, the Leaky FXLMS (LFXLMS) algorithm, and other modified LMS-based algorithms have been developed and utilized to combat the ANC problem. All of these algorithms enjoy great performance when the signal...

A popular approach for Active Noise Control (ANC) problems has been the use of the adaptive Filtered-X Least Mean Squares (FXLMS) algorithm. A fundamental problem with feedforward design is that it requires both reference and error sensors. In order to reduce the size, cost and physical complexity of the control system a feedback controller can be utilized. In contrast with FXLMS a feedback con...