The Algorithms of Adaptive Active Noise Control Systems in a Duct

On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.

IMPROVEMENT ON STABILITY OF ADAPTIVE NOISE CONTROL SYSTEM BY USE OF GRADIENT BACK-ADJUSTING FLMS ALGORITHM

GBFLMS algorithm is posed to improve the stability and performance on noise reduction of adaptive noise control (ANC) system. Theoretical analysis shows that stability and precision in convergence of FLMS algorithm can be improved by GBFLMS. By iterative calculation of back adjusting g radient, the calculating operation of GBFLMS can be nearly the same as that of FLMS. An ANC system with GBFLMS has good behavior for noise reduction of compressor chamber on refrigerator. At the port of heat dissipation, noise level has been reduced 20 dB, about double of that got by the system with FLMS.

Adaptive Active Control of Low Frequency Noise in a Duct

Aim To study a method realizing noise control for a physical model of progressive wave in a duct. Methods A mathematical model was constructed and a transfer function of the adaptive system for noise control was also worked out; moreover, the effects of some algorithms such as RLS,LMS and LSL on noise control were analyzed and compared. Results Without the feedback of sound, the mean noise reduce value(MNRV) of 27 5 dB for broadband noise from 0 to 500?Hz in frequency were achieved. When acoustic feedback took place and an air stream loudspeaker was used, the MNRV was only about 4 9?dB. But if the loudspeaker had a plain frequency feature, MNRV was improved by 10 2?dB. Conclusion The technique is applied to ruducing the noise from engines' exhausted gas pipes. It is, in principle, used for noise cancelling in a closed three dimensional space.

Active control of interior noise within an irregular enclosure under the cooperation of point force and Incident wave

A new modeling method is developed for the active control of interior noise within an irregular three-dimensional cavity under the cooperation of point force and incident wave. The validity of this method is verified by a regular cuboid enclosure. With global and local performance functions, good results are obtained in the active control of noise within the irregular enclosure according to numerical investigations.

Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception

The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies.However,some micro-motors may exhibit design deficiencies,component wear,assembly errors,and other imperfections that may arise during the design or manufacturing phases.Conse-quently,these micro-motors might generate anomalous noises during their operation,consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers.Automobile micro-motors exhibit a diverse array of structural variations,consequently leading to the manifestation of a multitude of distinctive auditory irregularities.To address the identification of diverse forms of abnormal noise,this research presents a novel approach rooted in the utilization of vibro-acoustic fusion-convolutional neural network(VAF-CNN).This method entails the deployment of distinct network branches,each serving to capture disparate features from the multi-sensor data,all the while considering the auditory perception traits inherent in the human auditory sys-tem.The intermediary layer integrates the concept of adaptive weighting of multi-sensor features,thus affording a calibration mechanism for the features hailing from multiple sensors,thereby enabling a further refinement of features within the branch network.For optimal model efficacy,a feature fusion mechanism is implemented in the concluding layer.To substantiate the efficacy of the proposed approach,this paper initially employs an augmented data methodology inspired by modified SpecAugment,applied to the dataset of abnormal noise sam-ples,encompassing scenarios both with and without in-vehicle interior noise.This serves to mitigate the issue of limited sample availability.Subsequent comparative evaluations are executed,contrasting the performance of the model founded upon single-sensor data against other feature fusion models reliant on multi-sensor data.The experimental results substantiate that the suggested methodology yields heightened recognition accuracy and greater resilience against interference.Moreover,it holds notable practical significance in the engineering domain,as it furnishes valuable support for the targeted management of noise emanating from vehicle micro-motors.

Simulation on a Car Interior Aerodynamic Noise Control Based on Statistical Energy Analysis

How to simulate interior aerodynamic noise accurately is an important question of a car interior noise reduction. The unsteady aerodynamic pressure on body surfaces is proved to be the key effect factor of car interior aerodynamic noise control in high frequency on high speed. In this paper, a detail statistical energy analysis （SEA） model is built. And the vibra-acoustic power inputs are loaded on the model for the valid result of car interior noise analysis. The model is the solid foundation for further optimization on car interior noise control. After the most sensitive subsystems for the power contribution to car interior noise are pointed by SEA comprehensive analysis, the sound pressure level of car interior aerodynamic noise can be reduced by improving their sound and damping characteristics. The further vehicle testing results show that it is available to improve the interior acoustic performance by using detailed SEA model, which comprised by more than 80 subsystems, with the unsteady aerodynamic pressure calculation on body surfaces and the materials improvement of sound/damping properties. It is able to acquire more than 2 dB reduction on the central frequency in the spectrum over 800 Hz. The proposed optimization method can be looked as a reference of car interior aerodynamic noise control by the detail SEA model integrated unsteady computational fluid dynamics （CFD） and sensitivity analysis of acoustic contribution.

Active Noise Control of the Heavy Truck Interior Cab

In order to control the noise of the heavy truck interior cab effectively, the active noise control methods are employed. First, an interior noise field test for the heavy truck is performed, and frequencies of interior noise of this vehicle are analyzed. According to the spectrum analysis of acquired noise signal, it is found out that the main frequencies of interior noise are less than 800Hz. Then the least squares lattice （LSL） algorithm is used as signal processing algorithm of the controller and a closed-loop control DSP system, based on TMS 320VC5416, is developed. The residual signal at driver＇s ear is used as feedback signal. Lastly, the developed ANC system is loaded into the heavy truck cab, and controlling the noise at driver＇ s ear for that truck at different driving speeds is attempted. The noise control test results indicate that the cab interior noise is reduced averagely by 0.9 dBA at different driving speeds.

Analysis and active control of low frequency booming noise in car

For Lightweight body,sound radiation and sound insulation performance have negative effects on interior noise by the deterioration of local stiffness and modality.So the research on the active control of vibration and noise for car body panels is useful for engineering.Analysis and active control of booming noise in car is researched by using a new active damping vibration reduction technology named smart constrained layer damping(SCLD).According to the vibration characters of body roof,an optimal placement of actuators is distributed.Based on dSPACE hardware in loop environment,an adaptive active control system is designed.Selecting vibration signals of engine mounting point as the reference input of adaptive controller,an active control experiment of booming noise for mini-car is carried out.Experimental results show that,when the engine speed is at 3700 RPM and4250RPM,the interior booming noise decreases 4.2dB(A),and 3.5dB(A) separately.It proposes new methods and techniques for intelligent control of car body NVH in the future.

Synchronization between two different noise-perturbed chaotic systems with unknown parameters

In this paper, a general method of synchronizing noise-perturbed chaotic systems with unknown parameters is proposed. Based on the LaSalle-type invariance principle for stochastic differential equations and by employing a combination of feedback control and adaptive control, some sufficient conditions of chaos synchronization between these noise-perturbed systems with unknown parameters are established. The model used in the research is the chaotic system, but the method is also applicable to the hyperchaotic systems. Unified system and noise-perturbed RSssler system, hyperchaotic Chen system and nolse-perturbed hyperchaotic RSssler system are taken for illustrative examples to demonstrate this technique.

Experimental Study of a Modified Command Governor Adaptive Controller for Depth Control of an Unmanned Underwater Vehicle

Command governor–based adaptive control(CGAC)is a recent control strategy that has been explored as a possible candidate for the challenging task of precise maneuvering of unmanned underwater vehicles(UUVs)with parameter variations.CGAC is derived from standard model reference adaptive control(MRAC)by adding a command governor that guarantees acceptable transient performance without compromising stability and a command filter that improves the robustness against noise and time delay.Although simulation and experimental studies have shown substantial overall performance improvements of CGAC over MRAC for UUVs,it has also shown that the command filter leads to a marked reduction in initial tracking performance of CGAC.As a solution,this paper proposes the replacement of the command filter by a weight filter to improve the initial tracking performance without compromising robustness and the addition of a closed-loop state predictor to further improve the overall tracking performance.The new modified CGAC(M-CGAC)has been experimentally validated and the results indicate that it successfully mitigates the initial tracking performance reduction,significantly improves the overall tracking performance,uses less control force,and increases the robustness to noise and time delay.Thus,M-CGAC is a viable adaptive control algorithm for current and future UUV applications.

A New Fully Differential Adaptive CMOS Line Driver Using Fuzzy Controller Suitable for ADSL Modems

In this paper, a new principle for an adaptive line driver using Fuzzy logic is presented. This type of line driver can adapt its output impedance and gain, automatically to the applied load using a fuzzy logic controller (FLC). This results in automatically corrected output impedance for different cables with terminations. Also, the line driver output impedance and gain become insensitive to process and line variations. As an example, a line driver for ADSL application has been designed. The circuit operates from a 3.3 v in a 0.35 um standard CMOS technology. The power consumption of FLC is about 1 mW. The circuit dissipates 106 mW and exhibits a -62 dB THD for a 3.2-Vpp signal at 5 MHz across a 75 ohms Load. It has a relatively high -3 dB bandwidth (240 MHz) with good phase margin of about 67 degrees in a 10 pF load capacitor.

低计算量的窄带ANC算法优化及实车DSP系统试验分析

车内主动噪声控制中常使用的传统滤波-x最小均方(Filtered-x Least Mean Square,FxLMS)算法由于计算复杂度高,往往导致系统硬件算力不足,降噪效果不理想。文章提出一种基于改进局部次级通路建模方法的自适应陷波(Local-secondary-path Filtered-x Least Mean Square,LFxLMS)算法及其相应的窄带主动噪声控制(LFxLMS-based Narrowband Active Noise Control,LFx-NANC)系统。所提出的改进局部次级通路建模方法具有更高的建模精度,且该系统相较于传统系统大大降低了计算复杂度。通过基于Matlab软件的仿真分析,验证了该系统对稳态及非稳态多谐波噪声的降噪性能。基于ADSP-21489控制器搭建车内双通道LFx-NANC系统,实现了在稳态工况下主驾位置处二、四、六阶降噪量分别达到34.67、21.41、10.29 dB(A);在加速工况下主驾位置处总声压级和二阶降噪量分别达到6.01 dB(A)和20.40 dB(A),同时在其他位置均有较好的降噪效果。文中提出的方法为主动噪声控制的工程应用提供了参考。

基于优化聚类的人工源电磁法数据信噪分离方法

为了降低强电磁干扰对人工源电磁法(Controlled Source Electromagnetic Method,CSEM)有效信号的影响,改善CSEM实测数据处理结果因人而异且效率低的不足,本文针对CSEM有效信号周期性特征提出了一种加权自适应带宽均值漂移聚类(Weighted Adaptive Bandwidth Mean-Shift Clustering,WAB-MSC)信噪分离方法.首先在传统均值漂移聚类(Mean-Shift Clustering,MSC)算法的基础上增加核函数,降低处理结果对带宽选择的敏感度,提高算法的稳健性;其次结合实测CSEM数据的分布特征提出了一种基于局部密度梯度的带宽估计方法,实现了自适应带宽选择;最后通过仿真数据与实测数据对本文方法进行了验证,结果表明:本文方法能有效消除强电磁干扰对CSEM数据的影响,最大程度保留受噪声影响较小或未受噪声影响的数据,提高数据信噪比,降低强干扰噪声对CSEM初始资料的影响程度,获得更为真实的地电响应模型,为后续数据处理提供保障.

利用近场法向声能流调控远场低频线谱噪声的自适应控制方法

针对水下结构远场低频辐射噪声的控制问题,提出了基于水下结构近场法向声能流的噪声自适应控制方法。该方法以结构近场的法向声能流作为控制物理量,利用有源控制系统自适应调节次级声源,抑制结构法向方向上的能量传播,实现远场低频辐射噪声控制。以圆柱壳为模型的试验结果表明,在1000~3000 Hz的频段内,相比于传统的以近场声压作为控制物理量的有源控制方法,能流法能够在保证总辐射声功率不升高的前提下,实现远场周向52.8%~76.3%区域内的噪声控制,且频率越低,降噪范围越广。

一种稳健的窄带反馈型主动噪声控制系统

传统窄带反馈型主动噪声控制系统采用并型自适应滤波器结构,用于合成参考信号时,仍存在参考信号合成质量较差、滤波器权值的初值设置难度高、应对非平稳窄带噪声的能力不足等问题。为解决以上问题,通过引入高效的串并型自适应滤波器结构来合成参考信号,提出新型的窄带反馈型主动噪声控制系统,改善参考信号合成质量,进而提升系统的整体降噪性能。新型系统不仅有效解决了传统系统存在的参考信号合成质量较差的问题,还降低了滤波器权值的初值设置复杂度,同时还提升了系统应对非平稳窄带噪声的能力。实验结果表明,新型系统较传统系统的前后两部分的降噪量分别提升了7.89和9.18 dB。仿真和实验结果共同验证了提出的系统较传统系统具有更强的稳健性和降噪性能。

测量噪声下的高超声速飞行器组合观测鲁棒控制

针对高超声速飞行器大包络飞行时,风场等外部干扰和测量噪声导致控制系统性能变差甚至失稳的问题,提出了一种基于状态观测的高超声速飞行器鲁棒控制策略。考虑噪声和外部干扰影响下系统状态受扰的问题,设计了扩张状态观测器(ESO)和自适应Kalman滤波器(AKF)的特殊组合,在统计特性未知的噪声和外部干扰影响下准确估计系统状态。其中,自适应Kalman滤波器用于滤除噪声,为扩张状态观测器提供不受噪声影响的状态估计,扩张状态观测器连续为自适应Kalman滤波器提供外部干扰估计。基于系统状态估计设计线性高斯二次型控制器,实现高超声速飞行器精确轨迹跟踪。最后,通过仿真验证了该方法在测量噪声及风干扰影响下的鲁棒跟踪能力。

Sigmoid变换的滤波-x四元数最小均方算法

滤波-x最小均方(Filtered-x Least Mean Square,FxLMS)算法是主动噪声控制系统中常用的算法,对中低频噪声有较好的控制作用,但在某些环境噪声中传统的算法可能达不到期望的抑制效果。提出一种基于sigmoid变换的滤波-x四元数最小均方算法,该算法利用四元数的空间特性使噪声信号在超复数域内部相互耦合和关联,并通过sigmoid函数对误差信号进行非线性变换来约束噪声信号以减低对权值更新的影响力度,避免权值在更新过程中发散,从而实现优异的收敛性能以及增强的鲁棒性。同时通过研究步长分析该算法的稳态特性,并在汽车、工厂噪声环境下验证提出算法性能的优越性,仿真结果支持了该结论。

高速列车包间噪声特性及降噪方案设计验证

针对高速列车包间噪声问题及降噪措施进行了相关研究。首先,通过线路试验分析了高速列车包间噪声特性,包括噪声频谱、声源识别和声品质,掌握了包间中心与铺面上方测点的噪声差异,明确了包间噪声控制目标;其次,基于统计能量分析方法建立了高速列车包间噪声预测模型,并进行了试验验证,开展了包间声能量贡献分析,确定了降噪方案设计指标,进而对车体进行了隔声方案优化设计;最后,通过包间噪声的迭代预测,确定了车体降噪方案的可行性,并进行了线路试验验证。结果表明,采用笔者设计的降噪措施,包间噪声总值降低了3.2 dB(A),包间原本较高的响度和粗糙度得到明显控制,尖锐度也略有降低,声品质得到了优化。

城市轨道车辆车内噪声的来源及控制

本文根据轨道车辆噪声源的不同共介绍了6种噪声,并着重介绍了在车辆运行时、制动时、静止时对车内噪声贡献最大的三种噪声来源(轮轨噪声、制动噪声、空调噪声)。通过对三种噪声的产生、传播途径及控制方式等方面的探究,得出可以通过主动降噪(对制动盘及闸片进行表面处理、在轨道及车轮上增加阻尼、优化通风管道结构等)降低车内噪声。同时,在声音传播过程中降低制动噪声级轮轨噪声的方式主要有:加装车底次地板、选用塞拉门、加装隔音吸引防寒材、喷涂阻尼浆等。