A Fuzzy Logic-Based Gain Scheduling Method for Online Feedback Path Modeling and Neutralization in Active Noise Control Systems

In conventional active noise control (ANC) system, the primary noise is attenuated over the frequency band of interest based simply on the error signal, which does not take into account the human perception of noise. Hence, researchers have developed psychoacoustic ANC system to improve its noise reduction performance from psychoacoustic point of view. However, in this psychoacoustic ANC system, there may be a disturbance that is uncorrelated with the primary noise at the error sensor, which can severely degrade the system's noise reduction performance. Hence, in this paper, a psychoacoustic hybrid ANC system is proposed, which can simultaneously control both the correlated primary noise and uncorrelated disturbance from psychoacoustic point of view. Loudness is used as the psychoacoustic criterion for evaluating the system's noise reduction performance. Simulation results show the effectiveness of the proposed psychoacoustic hybrid ANC system.

The main focus of this paper is the acoustic feedback path neutralisation during online operation of the single‐channel active noise control (ANC) systems. Invasive techniques, in which additive auxiliary noise is injected for online feedback path modelling (FBPM), degrade the noise‐reduction performance of ANC systems. In the existing methods for online FBPM, additive auxiliary noise with fixed variance is injected during all operating conditions of ANC systems. In this paper, a scheduling strategy is proposed to have time‐varying gain for additive auxiliary noise. The time‐varying gain is computed on the basis of (i) the convergence status of the FBPM filter, and (ii) the power of the interference term in the error signal of the FBPM filter. Simulation results show that, compared to the existing methods, the proposed method can better achieve the conflicting requirements of fast convergence of the FBPM filter, and reduced steady‐state power of the residual error at the error microphone. For sake of completeness, appendix presents typical simulation results when both the secondary path and feedback path are simultaneously identified during online operation of ANC systems.

A neural based controller with adaptive algorithm is presented in this paper to cancel the nonlinear broadband noise of an active noise control (ANC) system. The ways to avoid the premature saturation of backpropagation algorithm and design the optimal learning rate are also included in the paper to improve the noise reduction performance. The proposed neural filter can be easily implemented and also versatile to the other applications. Simulation results of canceling narrowband and nonlinear broadband noise show that the proposed method can effectively cancel the noise in an ANC system.

The intake system in an automotive engine has a short duct compared with that of the exhaust system. The filtered-x LMS (FX-LMS) algorithm has been applied to the active noise control (ANC) system in a short acoustic duct. This algorithm design is based on the FIR (finite impulse response) filter; however, it has a slow convergence issue due to a large number of zero coefficients. To improve the convergence performance, the step size of the LMS algorithm was modified from fixed to variable. However, this algorithm is still not suitable for the ANC system of a short acoustic duct because the reference signal is affected by the backward acoustic wave propagated from a secondary source. Therefore, the recursive filtered-u LMS algorithm (FU-LMS) based on the infinite impulse response (IIR) is developed to consider backward acoustic propagation. Generally, this algorithm has a stability problem. The stability issue was improved using an error-smoothing filter. In this paper, the recursive LMS algorithm with a variable step size and smoothing error filter is designed. This recursive LMS algorithm, the FU-VSSLMS algorithm, uses an IIR filter. With fast convergence and good stability, this algorithm is suitable for the ANC system in a short acoustic duct, such as the intake system of an automotive engine. This algorithm is applied to the ANC system of a short acoustic duct. The disturbance signals used as primary noise source are a sinusoidal signal embedded in white noise and the chirp signal, which has a variable instantaneous frequency. The test results demonstrate that the FU-VSSLMS algorithm has a superior convergence performance when compared with the FX-LMS and FX-LMS algorithms. The algorithm can be successfully applied to the ANC system in a short duct, such as the intake duct.

Online secondary path modeling algorithm without auxiliary noise for narrowband active noise control

Injecting auxiliary noise is an effective way of implementing online secondary-path modeling in active noise control (ANC) systems. The auxiliary noise may be scaled or multiplied by absolute value of one-sample-delayed residual noise signal. This strategy works well but more effort is needed to further reduce contribution of the injected auxiliary noise to the residual noise power. In this paper, we propose a novel strategy for the auxiliary noise injection in a typical narrowband ANC system, which not only significantly improves system convergence but also considerably reduce the steady-state residual noise power. The auxiliary noise to be injected is scaled by a signal which is obtained by passing a nonlinear function of one-sample-delayed residual noise signal through a lowpass filter. In the early stage of adaptation a large scaled auxiliary noise is injected to excite the secondary path to favor the secondary-path modeling, while in the steady state the scaled auxiliary noise becomes considerably small and hence contributes significantly less to the residual noise power. Extensive simulations demonstrate the effectiveness and superiority of the proposed strategy.

In active noise control (ANC) systems, the online secondary path modeling (OSPM) methods that use additive random noise are often applied. The additive random noise, however, contributes to the residual noise, and thus deteriorates the noise control performance of ANC systems. This paper proposes a new OSPM method with power scheduling of additive random noise. Here the OSPM filter is adapted using a variable step size (VSS) LMS algorithm already proposed by authors. Furthermore, the additive-random-noise power is scheduled based on the convergence status of an ANC system. Computer simulations demonstrate the effectiveness of the proposed method.

It is important to remove large environmental noise in measurement using SQUIDs without magnetic shielding. Here, the environmental noise has been reduced by an active noise control (ANC) system in the MHz radio-frequency region. The ANC system including a phase and amplitude control circuit has been designed to allow precise adjustments of the phase and the amplitude of the anti-phase wave against the noise waves in the MHz region. In this paper, a sinusoidal wave in the MHz range was used as the radio frequency interference. When a SQUID was used for a signal detector, the ANC system suppressed this sinusoidal wave to the white noise level by about 54 dB. The performance of the ANC system for the chemical detection by nuclear quadrupole resonance (NQR) was checked in a simulated environment. One was the case of a real AM radio broadcasting signal with the dummy NQR signal and another was the real NQR signal with a sinusoidal wave as noise. For both cases, the ANC showed good performance for MHz range noise cancellation.

Active Noise Control (ANC) is a widely adopted technology for reducing environmental noise across various scenarios. This paper focuses on enhancing noise reduction performance, particularly through the refinement of signal quality fed into ANC systems. We discuss the main wireless technique integrated into the ANC system equipped with some innovative algorithms in diverse environments. Instead of using microphone arrays, which increase the computation complexity of the ANC system, to isolate multiple noise sources to improve noise reduction performance, the application of the wireless technique avoids extra computation demand. Wireless transmissions of reference, error, and control signals are also applied to improve the convergence performance of the ANC system. Furthermore, this paper lists some wireless ANC applications, such as earbuds, headphones, windows, and headrests, underscoring their adaptability and efficiency in various settings.

The dynamic behaviour of the turning process is nonlinear and time-varying owing to variations in cutting depth. This paper proposes an optimal predicted fuzzy PI gain scheduling controller to control the constant turning force (CTF) process with a fixed metal removal rate (MRR) under various cutting conditions. The predicted fuzzy PI gain scheduling control scheme consists of two parts: the fuzzy PI gain scheduling controller; and the grey predictor. First, the optimal parameters of both the grey predictor and the optimal PI gains corresponding to each desired cutting depth in the range of operation, are designed off-line by using the proposed optimal combined method, i.e. Taguchi-RGA method, which integrates the Taguchi method and a real-coded genetic algorithm (RGA). Then, before the parameters of both the grey predictor and the PI gains are scheduled on-line, by fuzzy inference in terms of the changes of cutting depth, the optimal set of triangular-type membership functions of the fuzzy inference mechanism for scheduling the parameters of both the grey predictor and the PI gains are also designed off-line by using the Taguchi-RGA method. Computer simulations are performed to verify the applicability of this optimal predicted fuzzy PI gain scheduling control scheme for controlling the CTF process with a fixed MRR under various cutting conditions. It is shown that such an optimal predicted fuzzy PI gain scheduling control scheme can achieve satisfactory performance and better results than those reported recently in the literature.

In practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN)-based simultaneous perturbation stochastic approximation (SPSA) algorithm, which functions as a nonlinear mode-free (MF) controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS) algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.

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.

Multichannel feedforward active noise control system combined with noise source separation by microphone arrays

Infant incubators are widely used in neonatal intensive care units (NICU) for the pre-term or sick babies. In this paper, we propose an active noise control (ANC) system integrated with infant cry detection and classification function for infant incubators. The incubator ANC system reduces the high noise level which results in numerous adverse health effects for infants, and the cry detector and analyzer monitors the infants' physical conditions using the same ANC system hardware. The infant crying signals picked up by the error microphone of ANC inside the incubator are detected, the signals' features are extracted by using linear prediction coding (LPC), and Mel-frequency cepstral coefficients (MFCC) methods. The database of different signals and their associated infant health features will be established through training phase. The signal classification algorithm is based on shortest distance and Bayesian classifier. Simulation is conducted to evaluate the performance of proposed system using a Giraffe incubator from GE Healthcare. The experiments show that the proposed ANC system can reduce the harmful noise and the cry classification algorithm can recognize infant cries in order to develop the innovative, low cost methods to monitor the infant's health conditions.

An important step for active noise control (ANC) systems to be practical is to develop model independent ANC (MIANC) systems that tolerate parameter variations in sound fields. Reliabilities and stabilities of many MIANC systems depend on results of online system identifications. Parameter errors due to system identifications may threaten closed-loop stabilities of MIANC systems. A self-learning active noise control (SLANC) system is proposed in this study to stabilize and optimize an ANC system in case identified parameters are unreliable. The proposed system uses an objective function to check closed-loop stability. If partial or full value of the objective function exceeds a conservatively preset threshold, a stability threat is detected and the SLANC system will stabilize and optimize the controller without using parameters of sound fields. If the reference signal is available, the SLANC system can be combined with a feedforward controller to generate both destructive interference and active damping in sound fields. The self-learning method is simple and stable for many feedback ANC systems to deal with a worst case discussed in this study.