Multivariable Control Performance Assessment Based on Generalized Minimum Variance Benchmark

This paper is concerned with the control performance assessment based on the multivariable generalized minimum variance benchmark.An explicit expression for the feedback controller-invariant(the generalized minimum variance)term of the multivariable control system is obtained,which is used as a standard benchmark for the assessment of the control performance for multi input multi output(MIMO)process.The proposed approach is based on the multivariable minimum variance benchmark.In comparison with the minimum variance benchmark, the developed method is more reasonable and practical for the control performance assessment of multivariable systems.The approach is illustrated by a simulation example and an industrial application.

Adaptive diagonal loaded minimum variance beamforming applied to medical ultrasound imaging

In order to enhance the robustness and contrast in the minimum variance(MV) beamformer, adaptive diagonal loading method was proposed. The conventional diagonal loading technique has already been used in the MV beamformer, but has the drawback that its level is specified by predefined parameter and without consideration of input-data. To alleviate this problem, the level of diagonal loading was computed appropriately and automatically from the given data by shrinkage method in the proposed adaptive diagonal loaded beamformer. The performance of the proposed beamformer was tested on the simulated point target and cyst phantom was obtained using Field II. In the point target simulation, it is shown that the proposed method has higher lateral resolution than the conventional delay-and-sum beamformer and could be more robust in estimating the amplitude peak than the MV beamformer when acoustic velocity error exists. In the cyst phantom simulation, the proposed beamformer has shown that it achieves an improvement in contrast ratio and without distorting the edges of cyst.

基于SRM模型的宽带MVDR快速算法研究

为满足潜艇等水下航行器宽带自适应波束形成算法的更高实时性要求,在经典的MVDR(Minimum Variance Distortionless Response)时域解析信号模型的基础上,设计一种基于SRM(Spiked Random Matrix)的协方差逆运算的快速估计算法。首先对宽带信号进行时域解析信号的构造,依据基于随机矩阵理论,然后通过对快拍信号的协方差矩阵的特征值进行调整和处理,实现精确的协方差矩阵的逆运算估计,避免了传统的复杂求逆运算,尤其适合多传感器和快拍样本受限的环境。仿真实验和海试数据证明,提出的算法波束指向性能更优,信号处理复杂度更低,随着阵元间距数的增加,性能获得明显的提升。

Source localization with minimum variance distortionless response for spherical microphone arrays

To improve localization accuracy, the spherical microphone arrays are used to capture high-order wavefield in- formation. For the far field sound sources, the array signal model is constructed based on plane wave decomposition. The spatial spectrum function is calculated by minimum variance distortionless response （MVDR） to scan the three-dimensional space. The peak values of the spectrum function correspond to the directions of multiple sound sources. A diagonal loading method is adopted to solve the ill-conditioned cross spectrum matrix of the received signals. The loading level depends on the alleviation of the ill-condition of the matrix and the accuracy of the inverse calculation. Compared with plane wave decomposition method, our proposed localization algorithm can acquire high spatial resolution and better estimation for multiple sound source directions, especially in low signal to noise ratio （SNR）.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

Robust Design for Generalized Minimum Variance Controllers

In order to improve the robustness and noise resistance of generalized minimum valance cothrol systems, several generalizedminimum variance control schemes are synthetically analyzed. The output variance caused by stochastic noise is decomposed to two parts. One part accords with the output variance of minboum vedance control and the other is the additional term of output variance causedby the control weighting factors. At the same time, the sensitivity function of modeling error is also deduced. A new robast design method that can minimize the sensitivity and the additional part of output variance is Presented by regulating variable parameters of contollers. The simulation results of self-tuning control show the effect of this method.

A Geometric Approach to Conditioning and the Search for Minimum Variance Unbiased Estimators

Our purpose is twofold: to present a prototypical example of the conditioning technique to obtain the best estimator of a parameter and to show that this technique resides in the structure of an inner product space. The technique uses conditioning of an unbiased estimator on a sufficient statistic. This procedure is founded upon the conditional variance formula, which leads to an inner product space and a geometric interpretation. The example clearly illustrates the dependence on the sampling methodology. These advantages show the power and centrality of this process.

SIMULATION STUDY OF GENERALIZED MINIMUM VARIANCE CONTROL FOR AN EXTRACTION TURBINE

In an extraction turbine, pressure of the extracted steam and rotate speed of the rotor are two important controlled quantities. The traditional linear state feedback control method is not perfect enough to control the two quantities accurately because of existence of nonlinearity and coupling. A generalized minimum variance control method is studied for an extraction turbine. Firstly, a nonlinear mathematical model of the control system about the two quantities is transformed into a linear system with two white noises. Secondly, a generalized minimum variance control law is applied to the system. A comparative simulation is done. The simulation results indicate that precision and dynamic quality of the regulating system under the new control law are both better than those under the state feedback control law.

基于MVC算法的机械爪控制器设计

传统机械手爪控制器多采用经典PID控制算法,尽管PID控制实施简单,但是很难保证手爪以最优的方式运动。基于最小方差控制(MVC)理论以手爪的波动方差为控制目标,通过求解丢番图方程获得控制器增益,实现手爪波动方差最小的控制效果。在实例仿真中与传统的PID算法进行比较,结果表明,MVC算法控制器有效地改善了动态性能,有较强的信号跟踪能力。

基于联合时空图拓扑结构的多通道语音MVDR增强算法

本文研究图频域内的多通道语音增强,利用图信号处理理论(GSP)构建一种时间-空间维度的联合图拓扑结构,在此基础上设计增强算法进行多通道语音消噪。具体而言,基于输入阵列某个麦克风输入帧间语音顶点信号的时间相关关系,构造时间维度上的一种图拓扑结构;同时针对多通道含噪语音,根据各通道接收信号的空间相关关系,构造空间维度上的一种图拓扑结构。基于时间和空间二种图拓扑构成的联合图拓扑结构,采用图频域内的最小方差无失真响应(MVDR)增强算法,进行多通道语音增强。仿真实验结果表明,在平均客观语音质量评估(PESQ)得分和平均拓展短时客观可懂度(ESTOI)评价指标下,本文所提出的基于联合图拓扑结构的MVDR波束形成(JG-MVDR)方法都优于常规图MVDR波束形成(GMVDR)方法和基于复高斯混合模型的MVDR波束形成(CGMM-MVDR)方法。

基于MVDR波束形成的FDA平台外干扰抑制

针对频控阵(frequency diverse array,FDA)在对与目标位置相近的平台外干扰进行抑制时,传统FDA与非线性频偏结合不能实现距离-角度去耦合、对数递增FDA主瓣展宽较大和汉明窗加权FDA距离维旁瓣过高的问题,在充分研究汉明窗加权的线性FDA(Hamming linear FDA,HL-FDA)方案的基础上,对4种频偏方案的性能进行了分析,提出采用FDA多输入多输出结构代替传统的一维均匀线性FDA结构,通过结合最小方差无失真响应(minimum variance distortion free respinse,MVDR)准则,将HL-FDA应用到MVDR自适应波束形成层面,对平台外干扰进行抑制。仿真验证了HL-FDA在结合MVDR自适应波束形成算法后能有效地对抗距离-角度二维平台外干扰。

Nonlinear total least-squares variance component estimation for GM(1,1)model

The solution of the grey model(GM(1,1)model)generally involves equal-precision observations,and the(co)variance matrix is established from the prior information.However,the data are generally available with unequal-precision measurements in reality.To deal with the errors of all observations for GM(1,1)model with errors-in-variables(EIV)structure,we exploit the total least-squares(TLS)algorithm to estimate the parameters of GM(1,1)model in this paper.Ignoring that the effect of the improper prior stochastic model and the homologous observations may degrade the accuracy of parameter estimation,we further present a nonlinear total least-squares variance component estimation approach for GM(1,1)model,which resorts to the minimum norm quadratic unbiased estimation(MINQUE).The practical and simulative experiments indicate that the presented approach has significant merits in improving the predictive accuracy in comparison with control methods.

基于未知风荷载作用下拉索振动位移的拉索MR阻尼器失效破坏的原位识别方法

MR阻尼器在长期服役过程中可能由于各种因素导致阻尼器失效破坏。现有的对MR阻尼器的性能识别多为离线方法,需将阻尼器从斜拉索上拆卸下来后进行人工检测。该文提出一种基于斜拉索在未知风荷载作用下振动位移,对在役失效无模型的MR阻尼器作用力进行原位在线识别的方法。首先,建立模态坐标下斜拉索-阻尼器系统运动方程及观测方程,并进行模态截断以减少未知变量的数量。然后,将模态风荷载建模为随机游走过程并加入系统的增广状态中,并视MR阻尼器的作用力视为作用在斜拉索上的‘附加未知输入’,采用最近提出的无直接反馈的平滑最小方差无偏Bayesian滤波,对结构增广状态和MR阻尼器作用力进行识别。通过识别数值模拟的MR阻尼器三种典型的失效破坏模式,验证所提方法的有效性。

基于MV基准界的多变量控制系统性能评价方法

为解决关联矩阵难以估计的问题,提出利用最小方差(MV)基准界评价多变量控制系统性能的方法.建立了另一最小方差基准下界,此新下界可通过过程数据和对角关联矩阵得到.几种建界方法相互结合,形成一套完整的、基于最小方差基准界的多变量控制系统性能评价理论体系.通过仿真实验验证了该评价方法的有效性.

单个压差式矢量水听器MVDR波束形成的优化研究

从矢量水听器和声压阵两个角度,研究了单个二维压差式矢量水听器的最小方差无失真响应(minimum variance distortionless response,MVDR)波束形成的优化技术。针对压差式矢量水听器半径波长比大于0.1时,利用它进行方位估计性能恶化的问题,提出了一种修正的单个压差式矢量水听器MVDR波束形成处理方法。仿真和湖上试验的结果表明:修正的压差式矢量水听器MVDR处理方法对阵元间距的变化具有更好的稳健性;在半径波长比小于0.1时具有更好的估计性能,在半径波长比大于0.1的一定范围内依然具有良好的方位估计能力。

基于光纤光栅阵列和MVDR算法的声发射定位

基于光纤光栅(FBG)传感器网络构建了声发射检测系统,并提出了最小方差无失真响应(MVDR)的声发射源定位方法。构建的系统由7个FBG传感器组成传感器线阵列,采用未经平坦的放大自发辐射(ASE)光源边缘滤波实现信号解调。利用Shannon小波变换从频散复杂的声发射信号中提取窄带信号,并基于MVDR算法扫描整个监测区域获取空间谱。根据空间谱函数计算输出值,并将计算的输出值作为像素值。最后,通过提取空间谱中的最大值的坐标确定声发射源的位置。在LY12铝合金板上进行了实验验证。结果表明,该方法在400mm×400mm的区域内,声发射定位的最大误差为9.4mm,平均误差为7.2mm,耗时小于3s。该系统具有较高的实时性和定位精度,是一种声发射源定位的新方法。

基于MVDR的阵列信号波束域预处理算法

针对基于常规波束形成(CBF)的波束域方法在波束覆盖区域外存在方位不定强干扰源时多目标方位估计(DOA)失效的问题,提出采用最小方差无畸变响应(MVDR)的波束域预处理算法。该算法根据阵列接收数据自适应调整阵元权值,在波束覆盖区域外干扰源方位形成零陷,从而减小干扰对波束输出结果的影响。分析表明靠近波束覆盖区域的强干扰源对波束转换增益的影响有限。该算法进一步提高了波束域处理方法的抗干扰能力,仿真结果验证了该算法的有效性。

基于均值-方差-偏度的配电网有功-无功随机模型预测控制

针对高比例具有不确定性的可再生能源接入对配电网安全经济运行带来的挑战,提出一种基于均值-方差-偏度的有功-无功随机模型预测控制协调优化策略。首先,在综合考虑有功和无功成本的基础上,基于Fish⁃er-z变换的拉丁超立方采样生成可再生能源出力场景集,进而构建表征不确定性下收益的期望-偏度模型和表征不确定性风险的方差模型。然后,建立权衡收益和风险的三阶近似矩效用函数,提出基于均值-方差-偏度的有功-无功随机模型预测控制协调优化框架。最后,基于线性化潮流方法对所提模型进行线性化处理,在改进的IEEE-37节点系统进行仿真验证。结果表明,所提策略能够有效应对可再生能源不确定性对配电网经济运行与电压质量的影响。

基于MMTMV方法的多变量时变扰动系统性能评估

工业控制系统性能评估关系到工业生产安全与企业绩效.工业过程扰动频繁且存在时变特性,多变量时变扰动系统缺少统一有效的性能评估方法.本文提出了基于多模型混合时变最小方差(Multi-model mixing time-variant minimum variance,MMTMV)的多变量系统性能评估方法.首先,根据扰动作用起止时间设定混合权重,基于多模型混合的思想利用混合权重与每一时变扰动特性设计多变量MMTMV控制器.然后,利用多变量MMTMV控制器得出各被控变量输出方差,将多变量MMTMV控制器下被控变量的平均方差作为性能评估的基准.最后,本文通过在裂解过程和精馏过程中的控制性能评估应用,验证了多变量MMTMV评估方法的有效性.