A robust filtered-s LMS algorithm for nonlinear active noise control

The performance of a nonlinear active noise control (ANC) system based on the recently developed filtered-s least mean square (FsLMS) algorithm deteriorates when strong disturbances in the ANC system are acquired by the microphones. To surmount this shortcoming, a novel robust FsLMS (RFsLMS) algorithm is proposed for a functional link artificial neural network (FLANN) based ANC system. The new ANC system is least sensitive to such disturbances and does not call for any prior information on the noise characteristics. The results obtained from simulation study establish the effectiveness of this new ANC scheme.     

A novel feedback active noise control for broadband chaotic noise and random noise

The feedback active noise control (ANC) can be seen as a predictor, the conventional method based on filtered-x least mean square (FXLMS) algorithm can only be useful for linear and tonal noise, but for nonlinear and broadband noise, it is useless. The feedback ANC using functional link artificial neural networks (FLANN) based on filtered-s least mean square (FSLMS) algorithm can reduce some nonlinear noise such as chaotic noise, but the noise cancellation performance is not very well, at the same time, it is not useful to random noise. To solve the problem above, a new feedback ANC using wavelet packet FXLMS (WPFXLMS) algorithm is proposed in this paper. By decomposing the broadband noise into several band-limited parts which are predictable and each part is controlled independently, the proposed algorithm can not only suppress the chaotic noise, but also mitigate the random noise. Compared with FXLMS and FSLMS algorithms, proposed WPFXLMS algorithm also holds the best performance on noise cancellation. Numerous simulations are conducted to demonstrate the effectiveness of the proposed WPFXLMS algorithm.     

一种用于主动降噪耳机的权重滤波误差信号滤波-x最小均方算法*

针对传统FxLMS算法前馈自适应主动降噪耳机系统因果性条件不足时在宽带噪声环境中产生的高频噪声抬升问题,该文引入权重滤波误差信号FxLMS算法用于抑制高频噪声的抬升,但该算法带来了低频降噪量不足问题。因此,进一步提出将固定系数混合控制器与权重滤波误差信号FxLMS算法结合,在解决高频噪声抬升问题的同时,保证了良好的低频降噪量。基于DSP平台实现了提出的主动降噪耳机方案。实验证明,该方案针对宽带和单频等噪声都取得了较好的降噪效果。     

A gradient-free adaptation method for nonlinear active noise control

Active Noise Control (ANC) problems are often affected by nonlinear effects, such as saturation and distortion of microphones and loudspeakers. Nonlinear models and specific adaptation algorithms must be employed to properly account for these effects. The nonlinear structure of the problem complicates the application of gradient-based Least Mean Squares (LMS) algorithms, due to the fact that exact gradient calculation requires executing nonlinear recursive filtering operations, which pose computational and stability issues. One favored solution to this problem consists in neglecting recursive terms in the gradient calculation, an approximation which is not always without consequences on the convergence performance. Besides, an efficient application of nonlinear models cannot avoid some form of model structure selection, to avoid the well-known effects of overparametrization and to reduce the computational load on-line. Unfortunately, the standard ANC setting configures an indirect identification problem, due to the presence of the secondary path in the control loop. In the nonlinear case, this destroys the linear regression structure of the problem even if the control filter is linear-in-the-parameters, thereby making it impossible to apply the many existing model selection methods for linear regression problems. A simple and computationally wise low demanding approach is here proposed for parameter estimation and model structure selection that provides an answer to the mentioned issues. The proposed method avoids altogether the use of the error gradient and relies on direct cost function evaluations. A virtualization scheme is used to assess the accuracy improvements when the model is subject to parametric or structural modifications, without directly affecting the control performance. Several simulation examples are discussed to show the effectiveness of the proposed algorithms.     

Adaptive nonlinear neuro-controller with an integrated evaluation algorithm for nonlinear active noise systems

An adaptive nonlinear neuro-controller with an integrated evaluation algorithm for nonlinear active noise control systems is proposed to attenuate the nonlinear and non-Gaussian noises. Inspired by the structure of the Hammerstein or Wiener model, the proposed controller is realized by the static nonlinear memory function mapping on the basis of a single neuron. A generalized filtered-X gradient descent algorithm based on an integrated evaluation criterion is developed to adaptively adjust the weights of the controller, where the weighted sum of Renyi's quadratic error entropy and the mean square error is applied as the integrated performance index, which improves the performance of the adaptive algorithm by introducing the information entropy. In addition, the convergence of the proposed approach is analyzed, and the computational complexity among different methods is investigated. The proposed scheme can effectively attenuate the nonlinear and non-Gaussian noises and has a relative simple structure and less learning parameters. The simulation results demonstrate the validity of the proposed method for attenuating the nonlinear and non-Gaussian noises.     

Convex combination of nonlinear adaptive filters for active noise control

This paper proposes a nonlinear active noise control (ANC) system based on convex combination of a functional link artificial neural network (FLANN) and a Volterra filter. Simulation study reveals enhanced noise cancelation performance of the proposed ANC system over the ones based on its component filters.     

Improving performance of FxRLS algorithm for active noise control of impulsive noise

Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. New impulsive noise control algorithms based on filtered-x recursive least square (FxRLS) algorithm are presented. The FxRLS algorithm gives better convergence than the filtered-x least mean square (FxLMS) algorithm and its variants but lacks robustness in the presence of high impulsive noise. In order to improve the robustness of FxRLS algorithm for ANC of impulsive noise, two modifications are suggested. First proposed modification clips the reference and error signals while, the second modification incorporates energy of the error signal in the gain of FxRLS (MGFxRLS) algorithm. The results demonstrate improved stability and robustness of proposed modifications in the FxRLS algorithm. However, another limitation associated with the FxRLS algorithm is its computationally complex nature. In order to reduce the computational load, a hybrid algorithm based on proposed MGFxRLS and normalized step size FxLMS (NSS-FXLMS) is also developed in this paper. The proposed hybrid algorithm combines the stability of NSS-FxLMS algorithm with the fast convergence speed of the proposed MGFxRLS algorithm. The results of the proposed hybrid algorithm prove that its convergence speed is faster than that of NSS-FxLMS algorithm with computational complexity lesser than that of FxRLS algorithm.     

A waveform synthesis algorithm for active control of transformer noise: implementation

For the active control of the transformer noise, a newly developed adaptive algorithm based on waveform synthesis was proposed in [19], where a comparison of the performance of the proposed algorithm with the FXLMS algorithm made on a single channel system showed the feasibility of the algorithm. This paper describes the implementation of the proposed algorithm on a multiple channel adaptive control system, which is used to control the noise radiated by a small transformer in an anechoic chamber. The implementation shows that the proposed algorithm requires less memory and less computation load than a typical implementation of the FXLMS algorithm and that a controller realised with the proposed algorithm can effectively reduce transformer noise and be quite robust.     

On implementation of adaptive bilinear filters for nonlinear active noise control

In this technical note, the simplified diagonal-structure bilinear filtered-X least mean square (SDBFXLMS) and channel-reduced diagonal-structure bilinear filtered-X least mean square (CRDBFXLMS) algorithms are proposed. Computational complexity for each proposed algorithm is analyzed to show the significant computational reduction in comparison with the diagonal-structure bilinear FXLMS (DBFXLMS) algorithm. For L=15$L=15$ (memory length of the bilinear filter), P=2$P=2$ (the corresponding number of the diagonal channels for the SDBFXLMS algorithm is L+2P=19$L+2P=19$ and the corresponding number of the diagonal channels for the CRDBFXLMS algorithm is 2P=4)$2P=4)$, and M=64$M=64$ (memory length of the secondary path estimate), the SDBFXLMS algorithm achieves 45% and 40% reduction of multiplications and additions, respectively, while the CRDBFXLMS algorithm acquires 78% reduction of multiplications and 76% reduction of additions. Computer simulations validate the satisfied control performances of the proposed algorithms.     

一种参数优化的混沌信号自适应去噪算法

针对非线性自适应混沌信号去噪算法的参数优化问题, 考虑到最优滤波窗长受到不同因素的影响, 为提高该算法的自适应性, 提出一种滤波窗长自动最优化的判决准则. 依据混沌信号和噪声自相关函数的不同, 首先采用不同窗长对含噪混沌信号进行去噪, 然后计算每个窗长对应的残差自相关度(RAD), 最后通过对最小RAD所对应的窗长进行一定比例收缩实现窗长的最优化. 仿真结果表明, 该判决准则能够在不同条件下对滤波窗长进行有效的自动最优化, 提高了混沌信号去噪算法的自适应性.     

一种能量比调控的有源噪声控制算法

在前馈有源噪声控制系统中,建模信号与控制信号相互影响,建模信号的引入会导致系统降噪性能变差。为了减小建模信号的影响,提出一种基于能量比调控的次级通道在线建模有源噪声控制算法。利用控制过程与建模过程的误差能量比构造步长调控函数,分别调节控制过程与建模过程的步长值,从而减弱两者的相互影响。在次级通道建模过程中,对建模步长值采取分段调控的方法,并通过建模步长值的变化来调节建模信号,从而提升系统降噪性能。仿真结果表明,对于低频噪声信号的有源噪声控制,相比已有算法,提出的算法能获得较快的建模收敛速度和较高的降噪量。     

Adaptive variable step-size neural controller for nonlinear feedback active noise control systems

Adaptive filter techniques and the filtered-x least mean square (FxLMS) algorithm have been used in Active Noise Control (ANC) systems. However, their effectiveness may degrade due to the nonlinearities and modeling errors in the system. In this paper, a new feedback ANC system with an adaptive neural controller and variable step-size learning parameters (VSSP) is proposed to improve the performance. A nonlinear adaptive controller with the FxLMS algorithm is first designed to replace the traditional adaptive FIR filter; then, a variable step-size learning method is developed for online updating the controller parameters. The proposed control is implemented without any offline learning phase, while faster convergence and better noise elimination can be achieved. The main contribution is that we show how to analyze the stability of the proposed closed-loop ANC systems, and prove the convergence of the presented adaptations. Moreover, the computational complexities of different methods are compared. Comparative simulation results demonstrate the validity of the proposed methods for attenuating different noise sources transferred via nonlinear paths, and show the improved performance over classical methods.     

非线性系统混沌运动的神经网络控制

设计前馈反传神经网络控制非线性系统混沌运动的新方法.根据扰动参数模型输入输出数据,按照非线性学习算法训练网络产生系统稳定所需的小扰动控制信号,去镇定混沌运动,使嵌入在混沌吸引子中的不稳定周期轨道回到稳定不动点上.Hnon映射数值仿真结果表明,这种方法控制非线性混沌系统响应速度快、控制精度高 关键词： 混沌控制 神经网络 吸引子 非线性     

Lattice form adaptive infinite impulse response filtering algorithm for active noise control

In some situations of active noise control, infinite impulse response (IIR) filters are more suitable than finite impulse response (FIR) filters owing to the poles in the transfer function. A number of algorithms have been derived for applying IIR filters in active noise control; however, most of them use the direct form IIR filter structure, which faces the difficulties of checking stability and relatively slow convergence speed for noise composed of narrow-band components with large power disparity. To overcome these difficulties along with using the direct form IIR filters, a new adaptive algorithm is proposed in this paper, which uses and updates the lattice form adaptive IIR filter in an active noise control system. Full mathematical derivations of the proposed algorithm are presented, and the comparison between the proposed algorithm and the commonly used filtered-u LMS and filtered-v LMS algorithms shows the superiority of the proposed algorithm.     

一种改进的选频有源噪声控制算法

提出了一种改进的选频有源噪声控制(FSFANC)算法。一个FSFANC系统通过在信号输入通道中引入选频滤波器(FSF)选择一根待消除的谱线进行处理,多个FSFANC系统可以独立并行工作。本算法采用只含两个系数的有限冲击响应(FIR)结构对次级通道、初级通道、选频滤波器、控制滤波器等有关模块进行建模。理论分析和实验结果表明,当FSF中心频率一定时,即便噪声谱线频率与其有几赫兹的偏差系统也依然保持稳定。本算法结构简单,计算量小,收敛速度快,稳定性高,克服了现有FSFANC算法对频率偏差要求过于严格的限制。     

A compound secondary source for active noise radiation control

Based on the analysis of active noise control system with multi-channel monopole secondary sources, a kind of compound secondary source is proposed in this paper. The proposed compound secondary source consists of two closely located monopole sources with their distance much smaller than a wavelength. The characteristics of the compound source are analyzed, and the performances of the active noise control system with the compound secondary sources on the monopole primary sound field and sound radiated by a plate are investigated both numerically and experimentally. It has been found that the proposed compound secondary sources control system can provide higher noise reduction for free field noise radiation control with the same number of control channels. It is shown that the better performance in noise reduction of the compound secondary sources control system is mainly due to the directivity of the secondary sources where the energy radiation pattern of the compound sources is similar to that of the primary sources.     

应用于有源声辐射控制的一种组合次级声源

在分析多通道单极子次级源控制系统的基础上,提出一种幅度调节型组合次级声源,这种组合源由两个单极子声源构成,单极子声源的间距远小于声波的波长。文中就组合次级源在自由场中对不同初级声源辐射噪声的控制进行理论分析和实验验证,并与同等条件下的单极子次级源控制系统的降噪性能进行了比较分析。数值计算和实验结果均表明,对于不同的初级声场,在次级通道数相同的情况下,组合次级源控制系统可以得到比单极子次级源控制系统高的降噪量。     

一种调节反馈有源噪声控制系统水床效应的频域自适应算法*

伯德灵敏度积分决定反馈有源控制系统具有水床效应,即某个频段内的噪声衰减伴随着另一频段内的噪声放大。已有的反馈系统自适应算法没有明确考虑噪声放大量,非自适应算法考虑了噪声放大量但不能在线调整。为了使自适应反馈系统能明确调节噪声放大量,该文以控制器某个频段内的幅度响应小于指定阈值为约束条件,并作为"惩罚项"与误差信号能量相加得到代价函数,通过最速下降法最小化该代价函数得到了一种频域自适应算法。与已有算法在有源降噪耳机实例中的对比结果表明,该算法能够更为直接地对指定频段内的噪声放大量进行调节,从而在降噪量与噪声放大量之间取得折衷。     

A hybrid strategy to control uncertain nonlinear chaotic system

In this paper, a new method, based on firefly algorithm(FA) and extreme learning machine(ELM), is proposed to control chaos in nonlinear system. ELM is an efficient predicted and classified tool, and can match and fit nonlinear systems efficiently. Hence, mathematical model of uncertain nonlinear system is obtained indirectly. For higher fitting accuracy, a novel swarm intelligence algorithm FA is drawn in our proposed way. The main advantage is that our proposed method can remove the limitation that mathematical model must be known clearly and can be applied to unknown nonlinear chaotic system.     

Analogue active noise control

The aim of this article is to present in details all the stages of an analogue active noise control system. As the exemplary plant, an active personal hearing protection device is considered. In the introductory section the plant is presented and the state of the art is reported. In subsequent section, first a way of identification of a continuous-time model of the plant is described and performance limitations are analysed. Then, the procedure for designing optimal controller is proposed. It starts from analysing properties of the plant and choosing a suitable controller structure. Next, coefficients of the controller are found following the design premises. These coefficients serve as initial parameters in the optimisation process. For this process the performance index and appropriate constraints are built. Next, details on implementation of the controller using a chip with switched capacitors are given. Attenuation results of various noises are presented. Finally, cost of the hardware is evaluated.The methodology presented in this article can be considered as a detailed procedure for designing and practically realising cheap and easily tuneable controllers providing good noise attenuation and satisfying several constraints related to frequency band, stability margin, etc.