摘要
随着人们对汽车乘坐舒适性要求的不断提高,车内噪声已经成为评价汽车
乘坐舒适性的重要指标之一。传统的车内噪声控制主要采用优化汽车悬置隔
振、隔声、吸声等被动降噪措施,这些降噪方法对降低车内中高频段噪声作用
显著,可是,被动降噪方法不仅因车型不同而方法各异,而且降噪成本高、低
频降噪效果差。相对被动降噪方法而言,主动消声方法具有低频降噪效果好、
体积小、重量轻、易于控制等优点。随着现代控制技术的发展和电子芯片成本
的下降,有源消声逐渐显示了被动降噪无法比拟的优越性。
本文对车内低频噪声多次级声源有源消声系统进行了研究,提出了可在线
训练动态神经网络模型的车内多次级声源自适应有源消声方法,构建了车内多
次级声源有源消声系统,开发了车内自适应有源消声控制器和控制软件,并对
某轻型客车车内噪声进行了有源消声试验。本项研究为车内噪声有源控制系统
的实用化提供了基础,论文主要完成了以下研究工作:
提出了利用动态神经网络方法作为有源消声系统的核心算法,根据最速下
降原理对动态神经网络方法的权值修正进行了详细的推导,建立了反馈层输出
到隐层权值之间的数字表达式,提高了运算精度。以实测的 5 路振动加速度信
号为动态神经网络的输入信号,以正副驾驶员耳旁噪声信号作为待辨识信号,
应用 Matlab/Simulink 工具箱对动态神经网络的各个参数进行了优化选择。并
利用优化好的神经网络结构对不同转速下的车内噪声进行了辨识,结果表明所
优化的网络结构简单、参数匹配合理,能够满足车内噪声主动控制的要求。
建立了车内多次级声源有源消声系统模型并研究了车内有源消声的控制
策略。提出了适合车内多次级声源有源消声系统的多通道动态神经网络模型算
法(Multi-channel Dynamic Neural Network,简称 MDNN)。并在此基础上对
多次级声源有源消声系统中的声学路径问题进行了研究,提出了用抵消路径网
络对声学路径进行拟合的方法。由于动态神经网络具有实时性强的特点,该算
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摘要
法可实时辨识车内初级噪声信号和抵消路径, 并可进行动态神经网络的实时在
线训练,提高了系统适应汽车行驶中工况变化的能力,增强了系统的适应性和
实用性。
利用 Matlab/Simulink 工具箱,应用所提出的 MDNN 算法,构建了车内双
次级声源有源消声系统仿真模型,以实测的各点振动加速度信号和车内 2 通道
的噪声信号为基础数据对有源消声系统进行了仿真分析。结果表明,该系统有
良好的消声效果和稳定性。在不同发动机转速情况下,本文所构建的车内多次
级声源有源消声系统运行稳定、降噪效果显著,最大消声量可达到
17.8dB(Lin)。
对双次级声源有源消声系统进行了设计。对构成双次级声源有源消声系统
的主要部件:加速度传感器、电荷放大器、误差传声器、误差传声器前置放大
器、DSP 集成系统、功率放大板、次级声源扬声器等硬件进行了特性分析及
选取。以 DSP 作为核心硬件研制了可实现双次级声源有源消声功能的自适应噪
声主动控制器。开发了系统的控制软件,给出了系统控制流程。以某轻型客车
为试验对象构造了车内双次级声源有源消声系统。
为了克服理论分析方法难以对车内复杂空间声场进行准确描述的缺点,提
出了用试验的方法来分析次级声源和误差传声器的布放问题。着重分析研究了
车内双次级声源布放、次级声源与误差传声器的相对位置对车内消声区域和消
声效果的影响。结果表明:当误差传声器与次级声源的数目相同、误差传声器
位于次级扬声器的中心线上且与次级声源相距 0.2 米左右、两个次级声源相对
布置时消声效果最好。受次级声源本身特性和声波传播特性的限制,车内有源
消声系统的消声空间具有局域性,当需要对多个司乘人员所在的局域空间进行
消声时,应采用多次级声源系统。考虑到车内次级声源和误差传声器布置的可
行性,给出了客车、货车和轿车中次级声源和误差传声器的可行布置方案。
结合国标 GB1496-79 的有关规定,对本文所提出的 6 种次级声源和误差
传声器布放方案的消声效果进行了评价,为局域空间有源消声效果的评价提供
了一种尝试。
利用车内双次级声源有源消声系统,对车内低频噪声进行了有源消声试
验,对两组最佳次级声源布置方案的有源消声区域进行了深入的研究和探讨。
结果表明:在发动机转速为 1920r/min 时,有源消声系统能够在一定区域内都
达到较为明显的消声效果;在正副驾驶员双耳经常能够到达的区域内,消声量
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吉林大学博士学位论文
超过了 10dB(Lin)。
为了考察在不同发动机转速,以及不同车速道路行驶条件下的消声效果,
在不同发动机转速以及不同被试车行驶速度下,在某轻型客车内进行了有源消
声试验。结果表明,本文开发研制的有源消声系统能够在各种稳态工况下都实
现良好的消声效果。在不同发动机转速下,各误差传声器处的总消声量在 11.6
dB(Lin)~16.4dB(Lin)之间。在以不同车速行驶的工况下各误差传声器处的总
消声量也达到了 8.6
The interior noise in automotive cabin has been one of the important
indexes used to evaluate the ride comfort performance, as a result of the
increasing requirements of automotive performances. Now, the common
passive reduction noise measures in automobile, such as noise elimination,
sound absorption, vibration and sound isolation, etc, have good effect on
reducing the middle and high frequency noise. But when using the
above-mentioned measures to eliminate the vehicle interior noise, the
different methods must be applied on different types of vehicle, and these
measures also be expensive and have little effect on low frequency. While
the adaptive active noise control(AANC)is very effective on reducing low
frequency, and AANC has the less bulk , lighter weight than the passive
acoustic measures, and it is prone to control. Along with the development of
the modern control technology and descend of the COMS chip’s price,
AANC has more and more advantage than the common passive measures.
Therefore, a vehicle interior adaptive active noise control system of
multi-channel is researched in this paper. A vehicle interior adaptive active
noise control method of multi-channel based on online training dynamic
neural network model is proposed, and the AANC system of multi-channel
is established based on this method. The corresponding adaptive active
noise control program code is developed, and vehicle interior adaptive
active noise control experiments are carried out. This research has
provided a practicality base on applied work of the vehicle interior adaptive
active noise control system. The main research works completed in this
paper are as follows:
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吉林大学博士学位论文
After summarizing the feedforward active noise control system, a
viewpoint that taking the DNN method as the key arithmetic of the active
noise control is put forward. Based on the fastest declined theoretics, the
weight modification of dynamic neural network method is deduced
particularly, and the numeric expression between the feedback layer output
and hide layer is set up, which improves the operate precision. The input
signals of DNN are the real measured 5 kinds vibration acceleration signals
and the being-distinguished signals are the noise signals beside the drivers’
and co-pilots’ ear, Every parameter of the DNN has been optimized with
applying the Matlab/Simulink toolbox. And making use of optimized nerve
network structure, the interior noise in automotive cabin is distinguished on
different rotate speeds. The results indicate that the optimized network has
some excellences , such as simplified-structure, and reasonable
parameter-matching, which can achieve the pre-desire of the paper .
The model of vehicle interior multi-channel adaptive active noise
control system is set up and the control strategy is put forward. Based on
the control strategy of the above-mentioned model, the Multi-channel DNN
arithmetic—MDNN is put forward, which suit to vehicle interior
multi-channel adaptive active noise control. Then the sound path problem in
multi-channel adaptive active noise control system is studied with this
arithmetic, and a method in which this situation can be fit by counteracting
path network is proposed. Due to this dynamic neural network structure
being convenient for real-time application, the referent-signal and
counteract path can be distinguished in real-time by the arithmetic, and the
real online-practice of dynamic neural network can be carried out, which
improves the ability of suiting to the situation alteration in driving, and has
the strong suit-capability and practical- capability.
Using Matlab/Simulink toolbox, based on the arithmetic MDNN, the
mode of a vehicle interior adaptive active noise control system of
dual-channel is designed, and utilizing the testing points’ signals of
acceleration of vibration and the two noise signals in vehicle, the active
noise control system is simulated. The results of s
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