Adaptive recursive deconvolution and adaptive noise cancellation

In this paper we apply a recursive deconvolution method to active noise cancellation (ANC) in a linear system: the observation of the output of a linear system of relative degree one, read at discrete time instants, is fed to a deconvolution algorithm which identifies the disturbance (with the delay of one step). This information is used in order to reduce the effect of the disturbance itself. Deconvolution being an ill posed problem, a regularization parameter is to be introduced. The choice of the value of the parameter is a delicate issue. We show that, when studying ANC, the discrepancy principle (applied recursively) is a feasible method for the choice of the parameter.     

Active Narrowband Noise Control Systems Using Cascading Adaptive Filters

The disturbance picked up by error sensors can significantly degrade the steady-state performance of active noise control (ANC) systems for practical applications. This paper presents a cascading adaptive algorithm for removing uncorrelated disturbance and analyzes its performance in narrowband ANC systems. Theoretical analysis shows that the proposed algorithm improves the behavior of the adaptive filter in steady state. Computer simulations using the measured transfer functions validate its performance     

On Active Noise Control Systems With Online Acoustic Feedback Path Modeling

The presence of strong acoustic feedback degrades the convergence speed of the active noise control (ANC) filter, and in the worst case the ANC system may become unstable. A fixed feedback neutralization filter, obtained offline, can be used to neutralize the acoustic feedback. The feedback path, however, may be time varying, and we may need continual adjustments during online operation of the ANC system. This paper proposes a new method for online modeling of the acoustic feedback path in ANC systems. The proposed method uses three adaptive filters; a noise control filter, a feedback path modeling (FBPM) filter, and an adaptive noise cancelation (ADNC) filter. The objective of ADNC filter is to remove the disturbance from the desired response of FBPM filter. In comparison with the existing method, which works only for predictable noise sources, the proposed method can work, as well, with the broadband noise sources. The computer simulations are carried out for narrowband (predictable) (case I) and broadband (random) noise sources (case II). It is demonstrated that the proposed method performs better than the existing method in both cases     

Blind image deconvolution via dispersion minimization

In linear image restoration, the point spread function of the degrading system is assumed known even though this information is usually not available in real applications. As a result, both blur identification and image restoration must be performed from the observed noisy blurred image. This paper presents a computationally simple iterative blind image deconvolution method which is based on non-linear adaptive filtering. The new method is applicable to minimum as well as mixed phase blurs. The noisy blurred image is assumed to be the output of a two-dimensional linear shift-invariant system with an unknown point spread function contaminated by an additive noise. The method passes the noisy blurred image through a two-dimensional finite impulse response adaptive filter whose parameters are updated by minimizing the dispersion. When convergence occurs, the adaptive filter provides an approximate inverse of the point spread function. Moreover, its output is an estimate of the unobserved true image. Experimental results are provided.     

Stochastic parallel model adaptation: theory and applications toactive noise canceling, feedforward control, IIR filtering, andidentification

General stochastic parallel model adaptation problems that consist of an unknown linear time-invariant system and a partially or wholly tunable system connected in parallel, with a common input, are considered. The goal of adaptation is to tune the partially tunable system so that its output matches that of the unknown system, despite the presence of any disturbance which is stochastically uncorrelated with the input. The general formulation allows applications to adaptive feedforward control and adaptive active noise canceling with input contamination, in addition to output error identification and adaptive IIR filtering. It is shown that in all the applications, the goal of adaptation is met whenever a matching condition and a positive real condition are satisfied. A special case of the results therefore resolves the long-standing problem of the convergence and the unbiasedness of the output error identification scheme in the presence of colored noise. A simple general technique for analyzing the strong consistency of parameter estimation with projection is also developed     

New IIR filter-based adaptive algorithm in active noise control applications: Commutation error-introduced LMS algorithm and associated convergence assessment by a deterministic approach

In this study, commutation error (CE) is defined in adaptive infinite impulse response (IIR) filter-based ANC systems. CE is subsequently introduced into a new residual error to develop a new LMS-based ANC algorithm in an aim to liberate the restriction of slow adaptation posed on traditional ANC algorithms. A new deterministic analysis based on a linear time-varying system is performed to investigate convergence properties of the developed algorithm: (1) An optimal step size for the fastest convergence rate can be derived. (2) Given a persistent excitation condition and a step-size constraint, we find that the algorithm is uniformly asymptotically stable. Computer simulations indeed demonstrate a greatly improved convergence rate and efficient ANC performance for the developed algorithm as compared with that using the conventional algorithms. Experimental results verify the enhanced ANC performance in real applications. These together support the new IIR filter-based adaptive algorithm that includes CE for superior ANC performance with respect to the convergence rate and noise reduction level.     

Neural filtered-U algorithm for the application of active noise control system with correction terms momentum

This paper introduces a novel neural filtered-U recursive least mean square (NFURLMS) algorithm and its corresponding weight updating method to the application of active noise control (ANC) system. Instead of the complex designing procedures, the proposed approach uses few mathematical transfer functions to design the ANC system. The correction terms momentum to avoid the premature saturation of back-propagation algorithm and the way to design the optimal learning rate are also included in the paper to improve the noise reduction performance. In addition, the proposed method protects ANC systems against unstable poles such as occur in conventional filtered-U design. Several simulation results show that the proposed method can effectively cancel the narrowband and broadband noise in an ANC system.     

Particle swarm optimization based nonlinear active noise control under saturation nonlinearity

In this paper, a method is proposed to overcome the saturation non-linearity linked to the microphones and loudspeakers of active noise control (ANC) system. The reference microphone gets saturated when the acoustic noise at the source increases beyond the dynamic limits of the microphone. When the controller tries to drive the loudspeaker system beyond its dynamic limits, the saturation nonlinearity is also introduced into the system. The secondary path which is generally estimated with a low level auxiliary noise by a linear transfer function does not model such saturation nonlinearity. Therefore, the filtered-x least mean square (FXLMS) algorithm fails to perform when the noise level is increased. For alleviating the saturation nonlinearity effect a nonlinear functional expansion based ANC algorithm is proposed where the particle swarm optimization (PSO) algorithm is suitably applied to tune the parameters of a filter bank based functional link artificial neural network (FLANN) structure, named as PSO based nonlinear structure (PSO-NLS) algorithm. The proposed algorithm does not require any computation of secondary path estimate filtering unlike other conventional gradient based algorithms and hence has got computational advantage. The computer simulation experiments show its superior performance compared to the FXLMS, filtered-s LMS and genetic algorithms under saturation present at both at secondary and reference paths. The paper also includes a sensitivity analysis to study the effect of different parameters on ANC performance.     

有色噪声扰动下的随机控制问题研究

本文研究了有色噪声扰动下的线性二次随机控制问题,探讨了有色噪声与白色噪声随机 控制问题的等效化方法,利用准最优性能指标综合了准最优输出反馈控制,最后,利用原始系 统的系数矩阵导出了带有普遍意义的准最优控制算法框图.     

Ear Field Adaptive Noise Control for Snoring: An Real-time Experimental Approach

In this paper, an active noise control (ANC) system is developed to provide an effective and non-intrusive solution for reducing loud snoring to provide a quiet environment for a snorer's bed partner. An adaptive least mean square (LMS) algorithm optimized for different kinds of snore signals is introduced and theoretically analyzed. Also, a residual noise masking approach is proposed to further reduce the effect of the snore noise without interfering with the LMS algorithm. Computer simulations followed by real-time experiments are conducted to demonstrate the feasibility of the snore ANC systems based on a pillow setup. For the optimum effect based on the characteristics of human hearing, the performance of the proposed approach is evaluated by using the multi-channel feedforward ANC systems based on the filtered-X least mean square (FXLMS) algorithm. Compared with a traditional headboard setup for snoring noise control, the proposed snore ANC systems optimized for ear field operation yield much higher noise reduction around the ears of the snorer's bed partner.      

基于FXAFA算法的有源噪声消除系统

自适应介绍了基于TI公司提供的DSP芯片TMS320VC5402的自适应有源噪声控制（active noise control，ANC）系统，给出了系统的工作原理及其硬件结构，并详细说明了基于平均的FXAFA（Filtered-x Adaptive Filtering with Averaging）算法，给出了程序流程图和实验结果。通过实验证明，该系统有较好的降噪效果。     

Adaptive inverse control of linear and nonlinear systems using dynamic neural networks

In this paper, we see adaptive control as a three-part adaptive-filtering problem. First, the dynamical system we wish to control is modeled using adaptive system-identification techniques. Second, the dynamic response of the system is controlled using an adaptive feedforward controller. No direct feedback is used, except that the system output is monitored and used by an adaptive algorithm to adjust the parameters of the controller. Third, disturbance canceling is performed using an additional adaptive filter. The canceler does not affect system dynamics, but feeds back plant disturbance in a way that minimizes output disturbance power. The techniques work to control minimum-phase or nonminimum-phase, linear or nonlinear, single-input-single-output (SISO) or multiple-input-multiple-ouput (MIMO), stable or stabilized systems. Constraints may additionally be placed on control effort for a practical implementation. Simulation examples are presented to demonstrate that the proposed methods work very well.     

Subspace model identification under load disturbance with unknown transient and periodic dynamics

To overcome the influence from load disturbance with unknown transient and periodic dynamics, as often encountered when performing identification tests in engineering applications, a bias-eliminated subspace model identification method is proposed to realize consistent estimation, which can be used for both open- and closed-loop systems. By decomposing the output response into disturbed and undisturbed components, an oblique projection is subtly introduced to eliminate the disturbance and noise impact so as to obtain unbiased estimation on the deterministic system state matrices, while the disturbance response dynamics could be estimated. In particular, a specific algorithm based on minimizing the output prediction error is given to find out the disturbance period if exists, such that the disturbance effect can be eliminated by the above projection regardless of the disturbance waveform and magnitude. A shift-invariant approach is then given to retrieve the deterministic state matrices. Consistent estimation on the deterministic system matrices is analyzed with a proof. A benmark example from the literature and an industrial injection molding process are used to demonstrate the effectiveness and merit of the proposed method.     

Identifiability of the stochastic semi-blind deconvolution problem for a class of time-invariant linear systems

Semi-blind deconvolution is the process of estimating the unknown input of a linear system, starting from output data, when the kernel of the system contains unknown parameters. In this paper, identifiability issues related to such a problem are investigated. In particular, we consider time-invariant linear models whose impulse response is given by a sum of exponentials and assume that smoothness is the sole available a priori information on the unknown signal. We state the semi-blind deconvolution problem in a Bayesian setting where prior knowledge on the smoothness of the unknown function is mathematically formalized by describing the system input as a Brownian motion. This leads to a Tychonov-type estimator containing unknown smoothness and system parameters which we estimate by maximizing their marginal likelihood/posterior. The mathematical structure of this estimator is studied in the ideal situation of output data noiseless with their number tending to infinity. Simulated case studies are used to illustrate the practical implications of the theoretical findings in system modeling. Finally, we show how semi-blind deconvolution can be improved by proposing a new prior for signals that are initially highly nonstationary but then become, as time progresses, more regular.     

Adaptive rejection of disturbances having two sinusoidal components with close and unknown frequencies

The paper considers the problem of rejecting disturbances with two sinusoidal components in the case where the frequencies are unknown and closely spaced. A natural approach consists of cancelling the components using two separate adaptive algorithms combined in a single scheme. However, experiments in active noise control applications have shown that convergence using such an approach could be very slow. The alternative approach of this paper consists of representing the disturbance signal as a single sinusoid with time‐varying magnitude and phase. The theoretical basis and the limitations of this representation are first discussed. Then, an adaptive disturbance rejection algorithm is proposed and the resulting nonlinear system is analyzed using some approximations. Active noise control experiments demonstrate that the proposed algorithm has better convergence properties than an algorithm designed to cancel the two frequency components separately. In some cases, however, the cost is a small residual error on the output signal. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

An output-based adaptive iterative learning controller for high relative degree uncertain linear systems

In this paper, we derive an output tracking error model based on signals filtered from plant input and output, and then present a new output-based adaptive iterative learning controller for repeatable linear systems with unknown parameters, high relative degree, initial resetting error, input disturbance and output noise. The proposed controller solves the important robustness issues without assuming the bounds of uncertainties to be sufficiently small and can be applied to high relative degree plants without using output differentiation. Control parameters are updated between successive iterations so as to compensate for unknown system parameters and uncertainties. It is shown that the internal signals inside closed-loop learning system remain bounded and the output tracking error will asymptotically converge to a profile tunable by some design parameters. Furthermore, the learning speed is easily improved if the learning gain is increased.     

Disturbance attenuation over a first-order moving average Gaussian noise channel

In this paper, the problem of disturbance attenuation has been studied for a linear time-invariant feedback control system with a first-order moving average Gaussian noise channel. By applying the concept of entropy power, a lower bound of signal-to-noise ratio has been achieved which is necessary for stabilisation of a system with the limited channel input power constraint. Moreover, the problem of minimising the influence of a stochastic disturbance on the output has also been investigated, and the controller design method has been obtained by using Youla parameterisation and H₂ theory. It is shown that the minimum variance of the system output depends not only on the disturbance variance, noise variance and unstable poles, but also on the non-minimum phase zeros and channel parameter. Finally, the effectiveness of the proposed results is illustrated by a numerical example.     

Self-tuning weighted measurement fusion white noise deconvolution estimator and its convergence analysis

White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. For the multisensor linear discrete time-invariant stochastic systems with correlated measurement noises, and with unknown ARMA model parameters and noise statistics, the on-line AR model parameter estimator based on the Recursive Instrumental Variable (RIV) algorithm, the on-line MA model parameter estimator based on Gevers–Wouters algorithm and the on-line noise statistic estimator by using the correlation method are presented. Using the Kalman filtering method, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented based on the self-tuning Riccati equation. It is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steady-state white noise deconvolution estimator in a realization by using the dynamic error system analysis (DESA) method, so that it has the asymptotic global optimality. The simulation example for a 3-sensor system with the Bernoulli–Gaussian input white noise shows its effectiveness.     

Fast Affine Projection Algorithms for Filtered-x Multichannel Active Noise Control

In recent years, affine projection algorithms have been proposed for adaptive system applications as an efficient alternative to the slow convergence speed of least mean square (LMS)-type algorithms. Whereas much attention has been focused on the development of efficient versions of affine projection algorithms for echo cancellation applications, the similar adaptive problem presented by active noise control (ANC) systems has not been studied so deeply. This paper is focused on the necessity to reduce even more the computational complexity of affine projection algorithms for real-time ANC applications. We present some alternative efficient versions of existing affine projection algorithms that do not significantly degrade performance in practice. Furthermore, while in the ANC context the commonly used affine projection algorithm is based on the modified filtered-x structure, an efficient affine projection algorithm based on the (nonmodified) conventional filtered-x structure, as well as efficient methods to reduce its computational burden, are discussed throughout this paper. Although the modified filtered-x scheme exhibits better convergence speed than the conventional filtered-x structure and allows recovery of all the signals needed in the affine projection algorithm for ANC, the conventional filtered-x scheme provides a significant computational saving, avoiding the additional filtering needed by the modified filtered-x structure. In this paper, it is shown that the proposed efficient versions of affine projection algorithms based on the conventional filtered-x structure show good performance, comparable to the performance exhibited by the efficient approaches of modified filtered-x affine projection algorithms, and also achieve meaningful computational savings. Experimental results are presented to validate the use of the algorithms introduced in the paper for practical applications.      

An error passivation approach to filtering for switched neural networks with noise disturbance

In this paper, an error passivation approach is used to derive a new passive and exponential filter for switched Hopfield neural networks with time-delay and noise disturbance. Based on Lyapunov-Krasovskii stability theory, Jensen’s inequality, and linear matrix inequality (LMI), a new sufficient criterion is established such that the filtering error system is exponentially stable and passive from the noise disturbance to the output error. It is shown that the unknown gain matrix of the proposed switched passive filter can be determined by solving a set of LMIs, which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed switched passive filter.