管道噪声有源控制的声学特性研究Ⅱ实验部分

为验证在噪声有源控制系统作用下管道初级源与次级源之间驻波场的声场特性,在所设计的自适应有源消声实验系统上进行了低频段的单频消声实验,当各频率点分别取得了最大的消声效果后,采用驻波分离法对噪声有源控制系统作用下管道的声学特性进行了测试分析.实验结果验证:当达到最大消声效果时,在初级源与次级源之间驻波场声反射系数的模接近于1,从管道上游传播过来的声能量大部分被反射回去.     

自适应有源消声次级源安装角度优化试验与研究

针对一个基于Filtered-xLMS算法并应用M序列的在线自适应有源消声控制系统,研究了发动机排气管道有源消声控制中次级声源安装角度对消声效果的影响．通过改变次级声源的安装角度,在线检测其消声效果,实验验证了这种理论方法的有效性．最后得出如何优化次级声源的安装以改善发动机排气管道的消声效果．     

发动机排气电控消声实验系统的研究

为了提高发动机的动力性、经济性指标,同时改善其排放性能,根据发动机排气噪声的频谱特征,结合有源消声技术的特点,提出并设计了一种可消除低频噪声的发动机排气电控消声实验系统,并对该系统的消声机理进行了理论研究．电控消声实验系统的实验结果表明,在低频段取得了平均降噪３０ ｄＢ以上的效果．     

发动机排气自适应有源消声控制仿真实验

为了对发动机排气噪声进行实时控制,分析了发动机排气噪声产生的机理、噪声频谱特征,并利用基于自适应算法的有源消声控制技术对发动机排气噪声进行了实时控制分析实验．实验结果表明,所采用的自适应有源消声控制技术能够实时地对排气噪声进行控制．     

光机电一体化控制系统实验台的设计与研究

针对于当今高校实验课程设置中验证性、重复性实验偏多,培养学生分析、创造和动手能力的综合设计性实验过少的情况,我们选择了光机电一体化控制系统实验台的设计与研究这个课题。通过对本实验台的功用及实际课程中理论知识的分析,设计了一系列与单片机、PLC等课程知识结合紧密的实验,我们通过对实验过程和现象的分析,证明学生可以通过对光机电一体化控制系统实验台的操作过程中对理论知识进行更好的理解吸收。本文还总结了提高对光机电一体化控制系统深入学习在实际教学中重要性,以及在高校的实践教学中推广的必要性。     

管道有源消声的机理研究与元器件发展评述

综述了管道有源消声技术的声波干涉消声原理、阻抗控制消声机理以及传声器、扬声器和控制器元器件等的发展情况.重点论述了国外在管道有源消声技术研究中的研究成果和智能材料应用情况.预测了管道有源消声技术今后在消声机理研究与元器件研制方面的发展趋势.     

发动机排气噪声自适应控制系统的仿真研究

介绍了发动机排气噪声自适应控制系统的工作原理，并建立了系统模型，对以振动信号作为参考信号的消声效果进行了仿真，并且将发动机机体上不同点的振动信号作为参考输入进行了消声效果的比较．结果表明：当选取振动信号作为参考信号时，不仅要考虑振动信号与排气噪声信号的相关性，还要考虑振动信号的能量分布．     

管道噪声有源控制研究

提出了双初级传声器加分频器的单次级声源的噪声有源控制新系统,从理论上分析了该系统对管道噪声有源控制的可能性．用该系统对单频声、多频声和某风井的宽频带噪声进行了消声试验,分别取得了２５～３０ｄＢ,１５．８ｄＢ和１１．３ｄＢ的降噪量,试验证明了所提出的控制系统对管道噪声有源控制是可行和有效的     

神经网络BP算法在有源消声中的应用

为了克服传统自适应有源消声算法在应用中稳定性方面的不足,尝试将神经网络反向传播（ＢＰ）算法应用于有源消声技术。文中建立了基于ＢＰ算法的自适应有源消声（ＡＡＮＣ—ＢＰ）模型并给出该算法的递推公式。利用ＴＭＳ３２０Ｃ２５开发板实现了该算法功能。在半消声室中进行了单频和１００Ｈｚ带宽的消声实验,仅利用单个次级源结构便获得较好的消声效果。实验证明,基于ＢＰ算法的消声系统具有良好的稳定性。     

一种信息综合自适应管道有源消声系统的研究

探讨采用信号综合自适应系统的超稳定设计法实现管道有源消声的方法，解决了自适应状态观测器的设计以及如何实现纯滞后系统的自适应控制问题，最后给出了计算机仿真结果，从而验证了这种设计方法的可行性。     

Adaptive ANFIS-based filter for active control of sinusoidal primary noise in nonlinear path

Linear active noise control (ANC) system is promising in suppressing broadband and narrowband noise in a linear acoustic site.However,in some situations,the acoustic systems are nonlinear which make the primary noise being nonlinear distortion at the canceling point,and the linear ANC system maybe can not work well for these cases.This paper presents an adaptive network-based fuzzy inference system (ANFIS)-based nonlinear filter for active noise control,which is expected to be successfully applied to situations with nonlinear distortion.In the proposed adaptive control algorithm,the parameters are updated by gradient descent method.In the simulations,the secondary-path FIR model coefficients are estimated in advance by off-line system identification method,and one case for sinusoidal primary noise with nonlinear distortion is presented,which verifies the effectiveness of the proposed system in suppressing sinusoidal noise generated by rotating machines,e.g.,engines in vehicles.     

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.     

基于CMAC的有源噪声控制研究

提出一种神经自适应噪声有源控制的方法，把小脑模型关联控制器用于对噪声进行有源控制。对三维空间传播的宽频带噪声，利用小脑模型关联控制器实现有源噪声控制获得了良好的降噪效果。     

车内噪声控制技术研究现状及展望

综述了目前国内外车内噪声控制技术 ,重点论述了有源噪声控制技术和结构声的有源振动控制技术在车内噪声控制中的应用及其存在的问题 ,并预测了车内噪声控制技术的发展趋势。     

A flight experimental platform for synchrophasing control based on a small propeller UAV

Turboprop engine has the advantages of high efficiency and high thrust, but it has not been widely used in the civil field because of the noise excited by the low speed propellers. Propeller synchrophasing control is an active noise control method without increasing the weight of the airframe, which can attenuate the noise level in the cabin by controlling the relative phase among multiple propellers. It is generally accepted that altitude and airspeed have a great influence on the noise reduction effect of propeller synchrophasing, but there is no theoretical research about how these two factors affect the noise level. Therefore, flight experiment is considered to be a credible way. However, flight experiments of propeller synchrophasing control in turboprop aircraft have been accomplished in few countries due to enormous cost and inconvenience. An experimental platform of synchrophasing control based on a two-propeller small unmanned air vehicle (UAV) is proposed which can carry out the flight experiment research in a low-cost way. The phase angle sensor, the FOC-based scheme of motor driving, the all-slave synchrophasing control and the coordination between speed and phase difference control are presented in the UAV platform to meet its synchrophasing control precision requirement, experimental results prove that all of them can significantly enhance the performance of synchrophasing control. Noise characteristics of propellers are studied in the flight experiments, the noise predicted by the noise model is highly consistent with the actual measured noise, which verifies that the noise characteristics of small UAV accord with propeller signature theory. Based on the noise model, propeller synchrophasing has a steady effect to minimizing noise in the UAV platform. These show that the UAV platform is a feasible solution for propeller synchrophasing research.     

神经网络BP算法在噪声主动控制中的应用

在噪声主动控制系统中采用神经网络控制方法，给出了神经网络BP算法的一种变异算法。仿真实验表明，基于神经网络的噪声主动控制系统具有非常好的降噪效果和稳定性。     

一种小波包分解的FX结构全频带ANC系统

针对传统有源降噪系统在噪声与信号混合的复杂环境中无法有效工作的问题,设计了一种应用于舰艇艏端被动声纳平台的本艇自噪声有源抵消系统,从而提高声纳的作用距离．该系统采用基于小波包分解的FX结构,小波包这种正交变换方法降低了各分量之间的相关性,改善了参考输入矩阵的特征值分布,而其滤波器组的工作方式可以对不同频段的噪声任意细分,分别抵消,具有多子带系统的效果．通过与LMS算法相结合,使系统具备了灵活的全频带时频多分辨能力和较快的收敛速度．利用真实海试数据仿真试验,在较小的信号失真度状态下,取得了约12.2dB的全频带降噪量,进一步表明系统在复杂水下噪声场中的优势．该种结构提升了系统的噪声控制能力,具有较高的实用价值．     

噪声有源变结构控制

研究有源噪声控制（ＡＮＣ）问题,将滑模变结构技术引入有源噪声控制领域。提出了一种基于滑模变结构技术的噪声有源自适应控制方法,给出了一种在线参数自适应学习算法,证明了闭环控制系统在Ｌｙａｐｕｎｏｖ意义下的稳定性。仿真结果表明,基于滑模变结构技术的噪声有源自适应控制比传统的Ｆｉｌｔｅｒ－Ｘ控制更为有效。