An active noise control algorithm with gain and power constraints on the adaptive filter

Abstract

This article develops a new adaptive filter algorithm intended for use in active noise control systems where it is required to place gain or power constraints on the filter output to prevent overdriving the transducer, or to maintain a specified system power budget. When the frequency-domain version of the least-mean-square algorithm is used for the adaptive filter, this limiting can be done directly in the frequency domain, allowing the adaptive filter response to be reduced in frequency regions of constraint violation, with minimal effect at other frequencies. We present the development of a new adaptive filter algorithm that uses a penalty function formulation to place multiple constraints on the filter directly in the frequency domain. The new algorithm performs better than existing ones in terms of improved convergence rate and frequency-selective limiting.

Steady state behavior of normalized frequency domain adaptive filter in noncausal circumstances

“…The causality of the feedforward ANC system is destined to be violated in such a scenario. Similar to the signal used in [10], the source noise is generated by passing Gaussian white noise with unit variance through a lowpass filter whose transfer function is…”

“…Usually the NFLMS algorithm benefits from faster convergence speed for reference signal with large eigenvalue disparity of the autocorrelation matrix, which makes it very attractive in many application scenarios, one of which is the active noise control system [6][7][8][9][10]. However, it has been noted that the NFLMS cannot converge to the optimal Wiener filter [2,3], especially when the reference signal lags behind the expectation signal [4], a typical phenomenon happened in noncausal circumstances.…”

Control Effort Strategies for Acoustically Coupled Distributed Acoustic Nodes

“…Comparing (9) and (12), it can be seen that while the clipping l-DMEFxLMS algorithm only rescales the output, the rescaling l-DMEFxLMS algorithm rescales both the output and the filter coefficients. The dual rescaling prevents stability problems since the coefficients update is uncorrelated with the filter output when the clipping strategy is working [34,36]. It is important to take into account the fact that the system stability is ensured by applying the suitable constraints over the output signal, although too restrictive saturation levels could result in performance impairments.…”

“…Constraint techniques have been widely used in practical control systems [33,34]. Some of them may be intended for use in real scenarios to improve the processing efficiency [35][36][37] or even to reduce nonlinearity effects of the system [38]. A common way is to use a leakage during the updating of the control filter coefficients in the LMS algorithm [29].…”

Algorithms and implementations to overcome practical issues in active noise control systems

“…In contrast, if the selected leak is trivial, the control output may violate the constraint and result in a saturation problem. Hence, the steady-state performances of these leaky FxLMS algorithms are easily affected by the empirical choice of the leak factor or penalty coefficient [53]. Moreover, there is no guarantee that the output signal adheres to the imposed constraints [54].…”