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.

Separating iterative solution model of generalized nonlinear dynamic least squares for data processing in building of digital earth

Data coming from different sources have different types and temporal states. Relations between one type of data and another ones, or between data and unknown parameters are almost nonlinear. It is not accurate and reliable to process the data in building the digital earth with the classical least squares method or the method of the common nonlinear least squares. So a generalized nonlinear dynamic least squares method was put forward to process data in building the digital earth. A separating solution model and the iterative calculation method were used to solve the generalized nonlinear dynamic least squares problem. In fact, a complex problem can be separated and then solved by converting to two sub problems, each of which has a single variable. Therefore the dimension of unknown parameters can be reduced to its half, which simplifies the original high dimensional equations.

A Method for Assessing Customer Harmonic Emission Level Based on the Iterative Algorithm for Least Square Estimation

With the power system harmonic pollution problems becoming more and more serious, how to distinguish the harmonic responsibility accurately and solve the grid harmonics simply and effectively has become the main development direction in harmonic control subjects. This paper, based on linear regression analysis of basic equation and improvement equation, deduced the least squares estimation (LSE) iterative algorithm and obtained the real-time estimates of regression coefficients, and then calculated the level of the harmonic impedance and emission estimates in real time. This paper used power system simulation software Matlab/Simulink as analysis tool and analyzed the user side of the harmonic amplitude and phase fluctuations PCC (point of common coupling) at the harmonic emission level, thus the research has a certain theoretical significance. The development of this algorithm combined with the instrument can be used in practical engineering.

DQ变换和MUSIC算法在ITER磁体电源信号间谐波检测中的应用

随着国际热核聚变实验堆(International Thermonuclear Experimental Reactor,ITER)计划的逐步开展,保证ITER磁体电源系统的稳定运行显得尤为重要。文章采用将DQ变换和多信号分类(multiple signal classification,MUSIC)算法相结合的方法进行间谐波频率检测,信号的幅度和相位由最小二乘法来估计。DQ变换可以消除大幅度ITER基波分量,MUSIC算法可以通过矩阵特征分解检测出短数据条件下的谐波和间谐波,适用短时平稳的间谐波检测,两者相结合可以有效检测出大幅度基波附近存在小幅度间谐波。仿真实验表明,计算经DQ变换后检测出的ITER信号谐波频率时,取中间信号计算真实频谱较为正确,两侧信号则有较大的误差。

Iterative circle fitting based on circular attracting factor

An intuitive method for circle fitting is proposed. Assuming an approximate circle(CA,n) for the fitting of some scattered points, it can be imagined that every point would apply a force to CA,n, which all together form an overall effect that "draws" CA,n towards best fitting to the group of points. The basic element of the force is called circular attracting factor(CAF) which is defined as a real scalar in a radial direction of CA,n. An iterative algorithm based on this idea is proposed, and the convergence and accuracy are analyzed. The algorithm converges uniformly which is proved by the analysis of Lyapunov function, and the accuracy of the algorithm is in accord with that of geometric least squares of circle fitting. The algorithm is adopted to circle detection in grayscale images, in which the transferring to binary images is not required, and thus the algorithm is less sensitive to lightening and background noise. The main point for the adaption is the calculation of CAF which is extended in radial directions of CA,n for the whole image. All pixels would apply forces to CA,n, and the overall effect of forces would be equivalent to a force from the centroid of pixels to CA,n. The forces from would-be edge pixels would overweigh that from noisy pixels, so the following approximate circle would be of better fitting. To reduce the amount of calculation, pixels are only used in an annular area including the boundary of CA,n just in between for the calculation of CAF. Examples are given, showing the process of circle fitting of scattered points around a circle from an initial assuming circle, comparing the fitting results for scattered points from some related literature, applying the method proposed for circular edge detection in grayscale images with noise, and/or with only partial arc of a circle, and for circle detection in BGA inspection.

Improved scheme to accelerate sparse least squares support vector regression

The pruning algorithms for sparse least squares support vector regression machine are common methods, and easily com- prehensible, but the computational burden in the training phase is heavy due to the retraining in performing the pruning process, which is not favorable for their applications. To this end, an im- proved scheme is proposed to accelerate sparse least squares support vector regression machine. A major advantage of this new scheme is based on the iterative methodology, which uses the previous training results instead of retraining, and its feasibility is strictly verified theoretically. Finally, experiments on bench- mark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms, and this speedup scheme is also extended to classification problem.

Solving method of generalized nonlinear dynamic least squares for data processing in building of digital mine

Data are very important to build the digital mine. Data come from many sources, have different types and temporal states. Relations between one class of data and the other one, or between data and unknown parameters are more nonlinear. The unknown parameters are non random or random, among which the random parameters often dynamically vary with time. Therefore it is not accurate and reliable to process the data in building the digital mine with the classical least squares method or the method of the common nonlinear least squares. So a generalized nonlinear dynamic least squares method to process data in building the digital mine is put forward. In the meantime, the corresponding mathematical model is also given. The generalized nonlinear least squares problem is more complex than the common nonlinear least squares problem and its solution is more difficultly obtained because the dimensions of data and parameters in the former are bigger. So a new solution model and the method are put forward to solve the generalized nonlinear dynamic least squares problem. In fact, the problem can be converted to two sub problems, each of which has a single variable. That is to say, a complex problem can be separated and then solved. So the dimension of unknown parameters can be reduced to its half, which simplifies the original high dimensional equations. The method lessens the calculating load and opens up a new way to process the data in building the digital mine, which have more sources, different types and more temporal states.

基于改进初值带遗忘因子的递推最小二乘法的锂电池参数辨识

锂电池荷电状态(state of charge,SOC)的准确估计依赖于精确的锂电池模型参数。在采用带遗忘因子的递推最小二乘法(forgetting factor recursive least square,FFRLS)对锂电池等效电路模型进行参数辨识时,迭代初始值选取不当会造成辨识精度低、收敛速度慢的问题。为此,将电路分析法与FFRLS相结合,提出基于改进初值带遗忘因子的递推最小二乘法(improved initial value-FFRLS,IIV-FFRLS)。首先,通过离线辨识得到各荷电状态点对应的等效电路模型参数并进行多项式拟合;然后,利用初始开路电压(open circuit voltage,OCV)和OCV-SOC曲线获得初始SOC,代入参数拟合函数得到初始参数;最后,将初始参数带入递推公式得到IIV-FFRLS迭代初始值。对4种锂电池工况进行参数辨识,结果表明:与传统方法相比,IIV-FFRLS的平均相对误差、收敛时间分别减小58%、23%以上;IIV-FFRLS具有更高的辨识精度与更快的收敛速度。

基于位姿参数估计的多视角点云配准方法

传统的点云配准算法通过两点云数据之间的特征实现对应点配对,这种方法要求点云具有明确的特征,且存在计算量大、匹配时间长、配准精度低等问题,而ICP算法虽然应用广泛,但对初始值敏感。对此,提出了一种基于位姿参数估计的多视角点云配准方法(PPE-ICP)。首先通过分析误差的分布特性可证明误差极小值存在,使用A^(∗)搜索算法寻找误差极小值,降低误差传播的影响,为后续的参数估计提供较好的初值;其次将总体最小二乘估计引入点云配准,在不依赖点云数据的同时,使用少量参考点就能获得点云从目标坐标系到东北天坐标系的转换矩阵,完成点云位姿矫正,结合迭代最近点算法(ICP),实现点云精确配准。通过与FGR-ICP、FPFH-ICP、NDT-ICP、RANSAC-TrICP和KSS-ICP这5种方法在公开数据集和自制实验装置收集到的点云上进行对比实验,点云数据量为20000点时实现配准只需6.55 s,极大地降低了大数据量下点云配准的时间成本,在实地点云配准中平移误差最大不超过0.03 m,旋转误差控制在0.07°。实验结果表明,PPE-ICP对相似变换、残缺点云和低重复率具有较强的鲁棒性,在多视角点云配准中具有较高的配准效率和配准精度。

基于镜像修正FxLMS控制算法的船舶管路振动主动控制

针对船舶管路减振和抗冲击的需求,本文根据镜像修正自适应滤波算法,设计出了一种管路振动主动控制策略,能够有效地控制管路在低频下的振动,并且在次级通道发生突变时,控制系统可再次快速收敛,进行稳定控制。本文先对镜像修正自适应滤波算法进行理论研究,分析算法的迭代及控制过程;再通过仿真分别验证算法在不同参考信号输入下的收敛性及稳定性;最后搭建实验台架,通过试验验证算法的实际控制效果。试验结果表明:该控制策略在管路振动主动控制中能够降低15.37%的振动强度,比自适应滤波算法控制策略的控制效果好8.85%。所以镜像修正自适应滤波算法能够及时有效地进行管路振动控制。

基于TEASER算法的空间非合作目标位姿估计

基于点云的空间非合作目标位姿估计,常受到噪声影响.提出截断最小二乘估计与半定松弛(truncated least squares estimation and semidefinite relaxation,TEASER)与迭代最近点(iterative closest point,ICP)的结合算法,提升空间非合作目标位姿估计精度与鲁棒性.该方法包括粗配准与精配准两个环节:在粗配准环节中,基于局部点云与模型点云的方向直方图特征(signature of histogram of orientation,SHOT)确定匹配对,利用TEASER算法求解初始位姿;在精配准环节中,可结合ICP算法优化位姿估计结果.北斗卫星仿真实验表明:在连续帧位姿估计中,噪声标准差为3倍点云分辨率时,基于TEASER的周期关键帧配准方法的平移误差小于3.33 cm,旋转误差小于2.18°;与传统ICP方法相比,平均平移误差与平均旋转误差均有所降低.这表明所提出的空间非合作目标位姿估计方法具有良好的精度和鲁棒性.

基于ico_HEALPix网格的超高阶地球重力场建模方法

本文针对传统地理网格数据剖分的重力场模型构建在高纬度区域出现数据冗余的问题,将分层等积等纬度像素化(HEALPix)网格结构引入地球重力场解算中,提出了利用二十面体HEALPix(ico_HEALPix)网格的超高阶地球重力场建模方法,实现了全球3600阶次球谐位系数的高效构建,同时针对ico_HEALPix网格在球谐分析过程中法矩阵不是严格块对角化结构的问题,设计了迭代算法,有效提高模型构建的精度。试验表明,ico_HEALPix网格数据在数据量小于地理网格500万的前提下,通过迭代方法构建的全球地球重力场模型精度可达到优于地理网格的效果,球谐位系数误差阶RMS提升1~2个数量级,还解决了地理网格南北极点畸变和数据冗余的问题,提高了网格的数据利用率。

融合迭代平均滤波的RSSI室内定位算法

针对室内定位中,受无线通信距离和环境的影响,紫蜂(ZigBee)观测信号的随机噪声较为显著,导致定位结果的精度和稳定性较弱的问题,提出一种融合迭代平均滤波的RSSI室内定位算法:在数据采集过程中,采用高斯滤波对观测信号进行预处理,并根据接收信号强度指数测距理论建立起路径损耗模型,由此估算出信号接收节点到广播信标节点间的距离;然后,在定位解算过程中,在线性最小二乘算法的基础上融合迭代平均滤波算法,以抑制随机误差对定位结果的影响;最后,采用自研的CC2530 ZigBee模组对算法的可行性和稳定性进行实验验证。结果表明,等权最小二乘算法的点位精度约为0.64m,距离定权后,最小二乘算法的精度提升到0.52m;用等权最小二乘算法融合迭代平均滤波,精度也能达到0.5 m左右,与距离定权最小二乘的精度相当;同时,融合距离定权和迭代平均滤波2种方法的点位精度可以提高到0.35 m左右。

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

基于二维回波数据的距离-多普勒副瓣快速迭代抑制方法

针对距离-多普勒二维高副瓣严重影响强目标附近弱目标的高精度提取,现有方法对非网格目标提取效果不佳等问题,提出了一种基于二维回波数据的距离-多普勒副瓣快速迭代抑制算法。在迭代最小均方误差框架下,该方法基于网格化后的二维回波数据自适应地为每个距离-多普勒单元估计最佳权矢量,同时将同一距离门不同多普勒单元协方差矩阵的求逆转化为同一距离单元协方差矩阵的求逆,在不牺牲精度和检测性能的条件下大大降低了计算复杂度。仿真和实测数据验证了文中方法的有效性。

基于迭代优化的两帧随机相移干涉术

提出了一种由粗及精的无需预滤波的两帧先进迭代随机相移技术。基于最小化残差背景,首先利用采集条纹图的相关运算,在近似正交条件下估计相移和相位;然后利用这些求解过程中的中间参数在空域由条纹图数据估计相移和背景;最后,迭代上述过程直至收敛到预定义的精度,从而实现相位的精确求解。针对条纹图空域背景和振幅变化问题,利用条纹图子图降低背景空域变化对背景和相移估计的影响,并减小运算量。与现有的方法相比,本文方法由于提供了正确的初值,具有收敛快、计算快的特点。本文方法简单可靠,是一种实用的两帧相移干涉方法。

基于粒子群算法的γ能谱重叠峰解析

针对传统γ能谱分析中最小二乘法中的迭代算法对初始峰参量要求较严格,并且容易陷入局部最优等问题,提出了基于粒子群算法的γ能谱重叠峰解析方法,并且给出了重叠峰解析原理以及相应的算法.模拟和实测γ能谱重叠峰的解析结果均表明:在重叠峰的分离度较低的情况下,该方法能够得到较高的解析精度,并且具有较强的解析能力.另外,该方法还具有参量少、对初始参量要求宽松和算法易于实现等优点,而且收敛于全局最优解,因而是有效的γ能谱重叠峰解析方法.

基于IRLS的跳频模式下GTD散射参数提取和RCS重构

现代谱估计方法能够反演基于几何绕射理论(geometric theory of diffraction,GTD)的模型参数,但不能处理非均匀不完备的雷达散射截面(radar cross section,RCS)数据。此外,通过暗室测量获取完备的RCS数据也需要较大的时空开销。针对上述问题,提出一种基于迭代加权最小二乘(iteratively reweighed least squares,IRLS)的跳频模式下GTD散射参数提取和RCS重构方法。该方法将稀疏重构理论与GTD散射模型相结合,能够在RCS数据非均匀不完备的条件下反演散射参数和实现RCS重构。仿真数据和电磁计算数据用于验证所提方法的有效性,实验结果表明该方法对降低暗室步进频率RCS的测量成本和扩增雷达RCS数据具有重要意义。

一种基于罗德里格矩阵的最小二乘迭代坐标转换方法

本文结合罗德里格矩阵公式及其性质,在同等长度基准,即不考虑尺度因子的情况下,推导了基于罗德里格矩阵3参数的空间直角坐标转换模型、其线性化误差方程以及最小二乘迭代的严密公式。通过实测数据计算,不仅验证了该理论的可行性,还表明该算法具有精度高、稳定性强、适用性广,以及很强的理论研究价值等优点。

光点定位中的曲面拟合迭代算法

在光电检测系统中,需要对光点信号的位置实现亚像素定位。光纤从一端照明,由光纤另一端出射光点,由面阵CCD摄像机获取光点的灰度分布。基于最小二乘法,采用高斯曲面拟合算法拟合其分布,用高斯拟合函数的极值点作为光点的定位点。实验结果表明,采用多次迭代算法可以提高定位精度;拟合算法的稳定性(标准差)为0.7μm.高斯曲面拟合算法具有重复精度高、稳定性好的特点。