Adaptive inverse control of random vibration based on the filtered-X LMS algorithm

Random vibration control is aimed at reproducing the power spectral density （PSD） at specified control points. The classical frequency-spectrum equalization algorithm needs to compute the average of the multiple frequency response functions （FRFs）, which lengthens the control loop time in the equalization process. Likewise, the feedback control algorithm has a very slow convergence rate due to the small value of the feedback gain parameter to ensure stability of the system. To overcome these limitations, an adaptive inverse control of random vibrations based on the filtered-X least mean-square （LMS） algorithm is proposed. Furthermore, according to the description and iteration characteristics of random vibration tests in the frequency domain, the frequency domain LMS algorithm is adopted to refine the inverse characteristics of the FRF instead of the traditional time domain LMS algorithm. This inverse characteristic, which is called the impedance function of the system under control, is used to update the drive PSD directly. The test results indicated that in addition to successfully avoiding the instability problem that occurs during the iteration process, the adaptive control strategy minimizes the amount of time needed to obtain a short control loop and achieve equalization.

The implementation of Automatic Adaptive Equalizer Based on LMS Algorithm

In this paper,the inter-symbol interference and eliminating method are introduced.After analyzing the principle of adaptive equalization,we designed an adaptive equalizer using the LMS algorithm,and constructed a simulation system using MATLAB.Then we analyzed the convergence speed and mean square error characteristic of the adaptive equalizer by changing the step length factor to test the performance of the algorithm.

Active micro-vibration control based on improved variable step size LMS algorithm

The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.

Vibration Suppression for Active Magnetic Bearings Using Adaptive Filter with Iterative Search Algorithm

Active Magnetic Bearing(AMB) is a kind of electromagnetic support that makes the rotor movement frictionless and can suppress rotor vibration by controlling the magnetic force. The most common approach to restrain the rotor vibration in AMBs is to adopt a notch filter or adaptive filter in the AMB controller. However, these methods cannot obtain the precise amplitude and phase of the compensation current. Thus, they are not so effective in terms of suppressing the vibrations of the fundamental and other harmonic orders over the whole speed range. To improve the vibration suppression performance of AMBs,an adaptive filter based on Least Mean Square(LMS) is applied to extract the vibration signals from the rotor displacement signal. An Iterative Search Algorithm(ISA) is proposed in this paper to obtain the corresponding relationship between the compensation current and vibration signals. The ISA is responsible for searching the compensating amplitude and shifting phase online for the LMS filter, enabling the AMB controller to generate the corresponding compensation force for vibration suppression. The results of ISA are recorded to suppress vibration using the Look-Up Table(LUT) in variable speed range. Comprehensive simulations and experimental validations are carried out in fixed and variable speed range, and the results demonstrate that by employing the ISA, vibrations of the fundamental and other harmonic orders are suppressed effectively.

Adaptive Controller for Vehicle Active Suspension Generated Through LMS Filter Algorithms

最少的工具广场(LMS ) 适应过滤器算法在活跃暂停系统被使用。由调整适应过滤器的重量，最小的二次的表演索引被获得。为 two-degree-of-freedom 车辆暂停模型， LMS 适应控制器被设计。触发的团和没有安装弹簧的质量的加速作为暂停性能的评估目标被决定。为 LMS 适应控制暂停，与被动暂停相比，加速力量光谱在道路输入模型 decreased8-10 下面的触发的集体加速的密度在高频率回声乐队或低频率回声乐队预定。模拟结果证明 LMS 适应控制简单、显著地有效。它进一步证明活跃控制暂停系统能处于各种各样的操作条件改进骑的舒适和处理安全，并且方法为暂停系统的活跃控制是合适的。

ADAPTIVE EXPONENT SMOOTHING GRADIENT ALGORITHM

由于ＬＭＳ算法具有权调节时间延迟和低通滤波的特性，故提出一种新的自适应指数平滑梯度算法。研究表明，当信号是一个高斯平稳过程时，在参数域｛Ω１：α∈（０，１）｝×｛Ω２：β∈（０，∞）｝上，该算法渐进无偏收敛于维纳解。本文给出了算法收敛性能和性能失调的理论分析以及计算公式。计算机模拟的数值结果表明，该算法是有效的。

Parallel delayed LMS algorithm

A novel parallel delayed least-mean-square (PDLMS) algorithm is proposed by introduc-ing the parallel processing method into delayed LMS (DLMS) algorithm. Compared with DLMS, the al-gorithm presented has the property of smaller delay, higher throughput rate and faster convergence speed, while it also exhibits some de-correlation effect for the correlated input sequence. These prop-erties make it more suitable for the cases of high order filter with high convergence speed. At the same time, it can be mapped onto the high-speed and/or high-pipelined hardware structure directly.

A NEW ALGORITHM FOR ADAPTIVE LATTICE FILTERAND ITS APPLICATION IN THE SPEECH LINEARPREDICTIVE SYNTHESIS

In this paper, an adaptive line spectral pair filter is derived from an adaptive lattice filter. A least-mean-square(LMS) type adaptive algorithm used to calculate directly the line spectral pair(LSP) coefficients on a stage-by-stage basis is presented. Experimental results show that the algorithm has higher convergence rate and lower misadjustment as compared with the other algorithms. The LSP coefficients calculated by the algorithm have been used to carry out speech linear predictive synthesis, resulting in better results than PARCOR coefficients.

联合LMS滤波和对数MMSE的语音增强改进算法

由于固定步长的存在,传统LMS算法的收敛速度与稳态误差始终处于矛盾的状态,增强后的语音存在信噪比低和失真的问题。针对上述情况,提出一种联合LMS滤波和对数MMSE的语音增强改进算法。将LMS算法分为两个迭代周期,第一个周期使用变步长的LMS算法,第二个周期使用蝙蝠算法改进的LMS算法;使用改进后的LMS算法对带噪语音做初步的增强;使用对数MMSE对语音做进一步增强,去除改进的LMS算法增强后残留的噪声并还原出纯净的语音。实验结果表明,三种不同类型噪声下,该算法在SNR和PESQ指标上相比于传统的LMS和其他算法有更好的增强效果,具有更小的稳态误差和更好的噪声抑制效果,增强后的语音质量和听感得到提升。

阻尼器测试试验台的新变步长LMS自适应滤波器设计

【目的】提升阻尼器测试试验台的控制品质,进而得到更好的被测阻尼器参数曲线,需要对试验台反馈数据进行滤波提取。【方法】课题组通过对传统LMS自适应滤波器的研究,针对其步长选值问题,提出了一种基于麻雀搜索算法(Sparrow Search Algorithm,SSA)的变步长LMS算法,进而进行了新变步长LMS自适应滤波器设计。并通过Tent混沌映射优化了SSA算法在LMS自适应滤波器中的使用,加强了该算法的跟踪和收敛能力。【结果】仿真结果表明,优化后的算法较优化前误差总值下降约75.72%,优化效果较为显著。同时,相较于其他文献中的LMS自适应滤波算法,本设计中的算法能在快速收敛的同时保持较低的稳态误差,误差总值下降约35.17%。【结论】经过Tent混沌映射所得出的SSA算法初始种群能够提高LMS算法的跟踪能力、收敛速度和稳态精度。课题组设计的滤波器表现良好,具有较强的跟踪能力和稳态精度在低范围的保持能力,为阻尼器测试试验台性能优化和反馈数据的滤波提取提供了新的思路和方案。

暖通空调小流量风机机械振动信号自适应采集研究

当暖通空调小流量风机运行时,频繁产生的脉冲噪声增加了信号采集的复杂性并引入了干扰,使得采集到的振动信号含有大量不必要的噪声成分,降低了采集精度和准确性。为此,提出暖通空调小流量风机机械振动信号自适应采集方法。使用经验模态分解方法对振动信号进行处理,获得信号在时域和频域上的特征信息。根据振动信号的频率特征,设计自适应变采样算法在不同的时间段内动态地调整采样率,采集风机振动信号。使用数学形态滤波器调整信号的形状,去除脉冲干扰以提高采集信号的精度,并进一步优化信号的质量。实验结果表明,所提方法的振动信号自适应采集精度高,且采集时间短。

基于箕舌线的变步长LMS自适应算法

通过建立步长因子μ与误差信号 e之间的非线性关系 ,提出一种新的基于箕舌线的变步长 LMS算法 ,并将其应用于通信降噪。该算法除了具有传统固定步长 LMS算法计算量小、稳定性较好、简单、易于实时处理等优点外 ,计算机仿真结果表明 ,其收敛速度、稳定性以及跟踪速度优于 SVSLMS算法和 NLMS算法 ,且不需进行指数运算 ,计算复杂度低于 SVSLMS算法 ,用于通信降噪取得了较好的效果。

一种变步长凸组合LMS自适应滤波算法改进及分析

为了避免单个滤波器在收敛速度与稳态误差上相互制约,从而导致系统性能降低的问题,本文采用凸组合最小均方算法(Combined Least Mean Square,CLMS),将快速滤波器和慢速滤波器并联使用,同时为进一步改善CLMS算法的性能,对已有的变步长凸组合最小均方算法(Variable Step-size Convex Combination of LMS,VSCLMS)做出改进,提出了一种新的VSCLMS算法.在该算法中,对快速滤波器选用以最小均方权值偏差(Minimization of Mean Square Weight Error,MMSWE)为准则的按步分析的变步长滤波器;对慢速滤波器采用以稳态最小均方误差(Least Mean Square,LMS)为准则的固定步长滤波器.通过理论分析与仿真实验表明,该算法能够在噪声、时变以及非平稳的环境下保持较好的随动性能,且在各个阶段均保持良好的收敛性,与传统的CLMS、VSCLMS算法相比,不仅具有更快的收敛速度,而且拥有稳定的均方性能和较优的跟踪性能,为自适应滤波算法的研究提供了一条可行途径.

用于ADSL中的变步长LMS算法

本文给出了一种改进的LMS算法,该算法可应用于非对称数字用户线中实现回波抵消功能,从而在低频段的子通道内同时传输上行和下行的数据。新算法利用误差信号的相关值调节算法步长,具有较好的抗干扰性能,解决了算法收敛时间和稳态误差间的矛盾,为实际应用提供了更大的控制灵活性。仿真结果表明,在低信噪比的环境中,改进算法的收敛速度和稳态误差等性能指标都有较大的提高,并对随机出现的脉冲干扰表现出较好的鲁棒性。

一种变步长LMS自适应滤波算法及分析

本文对变步长自适应滤波算法进行了讨论 ,建立了步长因子 μ与误差信号e(n)之间另一种新的非线性函数关系 .该函数比已有的Sigmoid函数简单 ,且在误差e(n)接近零处具有缓慢变化的特性 ,克服了Sigmoid函数在自适应稳态阶段步长调整过程中的不足 .由此函数本文得出了另一种新的变步长自适应滤波算法 ,并且分析了参数α ,β的取值原则及对算法收敛性能的影响 .该算法有较好的收敛性能且计算量少 .计算机仿真结果与理论分析相一致 。

一种新的变步长LMS自适应滤波算法

传统LMS算法的优点是计算简单、易于实现，缺点是收敛速度慢，如果为加快收敛速度而增大步长因子μ，则会导致大的稳态误差，甚至引起算法发散。固定步长因子无法解决收敛速度和稳态误差之间的矛盾。本文通过建立步长因子μ与误差信号之间的非线性函数关系，得出一种新的变步长自适应滤波算法（SVSLMS）。理论分析和计算机仿真结果表明该算法的性能优于传统的LMS算法和NLMS算法。即在计算量增加不多的前提下，能同时获得较快的收敛、跟踪速度和较小的稳态误差。

基于改进的双曲正切函数变步长LMS算法

为了改进现有的变步长最小均方误差(least mean square,LMS)算法在低信噪比时性能较差的缺陷,提出了一种基于改进的双曲正切函数的变步长LMS算法,从理论分析和仿真实验两方面讨论了引入参数对算法收敛性、跟踪性、稳定性的影响及算法的抗干扰性。理论分析和仿真实验表明该算法在高低信噪比时均具有较快的收敛速度和跟踪速度以及较小的稳态误差和稳态失调,并且在低信噪比时该算法的收敛性、跟踪性、稳态性均优于其他多种变步长算法。

LMS自适应滤波器FPGA实现的新方法

针对用数字信号处理器(DSP)实现的自适应滤波器处理速度低、抗干扰性差和编写底层HDL代码用现场可编程门阵列FPGA实现开发效率低的缺点,该文利用最新DSP Builder工具建立了基于最小均方误差(LMS)算法的8阶二进制频移键控(2FSK)信号去噪自适应滤波器的模型,在EPF10K100EQC208-1器件上设计出了处理速度为36.63 MHz的8阶自适应滤波器,其速度是通过编写底层VHDL代码设计的自适应滤波器7倍,是采用DSP通用处理器TMS320C54X设计的自适应滤波器的25倍。

基于LMS算法的自适应均衡系统的仿真研究

描述了用仿真试验得出LMS自适应均衡滤波器的收敛性和跟踪性能与滤波器长度和迭代算法跳步两个重要的参数之间的定量关系,为此构建了有实用价值的系列时延扩展的传输环境和可变多径传输信道。建立了系统仿真模型、做出了仿真试验结果并分析了仿真试验结果的意义。