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.

Spline adaptive filtering algorithm based on different iterative gradients:Performance analysis and comparison

Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performance and computational complexity analyses are carried out also.Furthermore,compared with existing SAF algorithms,the influence of step-size and noise types on SAF algorithms are explored for nonlinear system identification under artificial datasets.Numerical results show that the SAF-Adagrad and SAFRMSProp algorithms have better convergence performance than some existing SAF algorithms(i.e.,SAF-SGD,SAF-ARC-MMSGD,and SAF-LHC-MNAG).The analysis results of various measured real datasets also verify this conclusion.Overall,the effectiveness of SAF-Adagrad and SAF-RMSProp are confirmed for the accurate identification of nonlinear systems.

AN ITERATIVE ALGORITHM FOR OPTIMAL DESIGN OF NON-FREQUENCY-SELECTIVE FIR DIGITAL FILTERS

This paper proposes a novel iterative algorithm for optimal design of non-frequency-selective Finite Impulse Response(FIR) digital filters based on the windowing method.Different from the traditional optimization concept of adjusting the window or the filter order in the windowing design of an FIR digital filter,the key idea of the algorithm is minimizing the approximation error by succes-sively modifying the design result through an iterative procedure under the condition of a fixed window length.In the iterative procedure,the known deviation of the designed frequency response in each iteration from the ideal frequency response is used as a reference for the next iteration.Because the approximation error can be specified variably,the algorithm is applicable for the design of FIR digital filters with different technical requirements in the frequency domain.A design example is employed to illustrate the efficiency of the algorithm.

Improved Adaptive Iterated Extended Kalman Filter for GNSS/INS/UWB-Integrated Fixed-Point Positioning

To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satellite system,inertial navigation system,and ultra wide band(UWB)is proposed.In thismethod,the switched global navigation satellite system(GNSS)and UWB measurement are used as the measurement of the proposed filter.For the data fusion filter,the expectation-maximization(EM)based IEKF is used as the forward filter,then,the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing.Tests illustrate that the proposed AIEKF is able to provide an accurate estimation.

Reservoir history matching and inversion using an iterative ensemble Kalman filter with covariance localization

Reservoir inversion by production history matching is an important way to decrease the uncertainty of the reservoir description. Ensemble Kalman filter （EnKF） is a new data assimilation method. There are two problems have to be solved for the standard EnKF. One is the inconsistency between the updated model and the updated dynamical variables for nonlinear problems, another is the filter divergence caused by the small ensemble size. We improved the EnKF to overcome these two problems. We use the half iterative EnKF （HIEnKF） for reservoir inversion by doing history matching. During the H1EnKF process, the prediction data are obtained by rerunning the reservoir simulator using the updated model. This can guarantee that the updated dynamical variables are consistent with the updated model. The updated model can nonlinearly affect the prediction data. It is proved that HIEnKF is similar to the first iteration of the EnRML method. Covariance localization is introduced to alleviate filter divergence and spurious correlations caused by the small ensemble size. By defining the shape and size of the correlation area, spurious correlation between the gridblocks far apart is alleviated. More freedom of the model ensemble is preserved. The results of history matching and inverse problem obtained from the HIEnKF with covariance localization are improved. The results show that the model freedom increases with a decrease in the correlation length. Therefore the production data can be matched better. But too small a correlation length can lose some reservoir information and this would cause big errors in the reservoir model estimation.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/PDR/geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.

Iterative filtered ghost imaging

It is generally believed that,in ghost imaging,there has to be a compromise between resolution and visibility.Here we propose and demonstrate an iterative filtered ghost imaging scheme whereby a super-resolution image of a grayscale object is achieved,while at the same time the signal-to-noise ratio(SNR)and visibility are greatly improved,without adding complexity.The dependence of the SNR,visibility,and resolution on the number of iterations is also investigated and discussed.Moreover,with the use of compressed sensing the sampling number can be reduced to less than 1%of the Nyquist limit,while maintaining image quality with a resolution that can exceed the Rayleigh diffraction bound by more than a factor of 10.

An iterative Wiener filtering method based on the gravity gradient invariants

How to deal with colored noises of GOCE （Gravity field and steady - state Ocean Circulation Explorer） satellite has been the key to data processing. This paper focused on colored noises of GOCE gradient data and the frequency spectrum analysis. According to the analysis results, gravity field model of the optima] degrees 90-240 is given, which is recovered by COCE gradient data. This paper presents an iterative Wiener filtering method based on the gravity gradient invariants. By this method a degree-220 model was calculated from GOCE SGG （Satellite Gravity Gradient） data. The degrees above 90 of ITG2010 were taken as the prior gravity field model, replacing the low degree gravity field model calculated by GOCE orbit data. GOCE gradient colored noises was processed by Wiener filtering. Finally by Wiener filtering iterative calculation, the gravity field model was restored by space-wise harmonic analysis method. The results show that the model＇s accuracy matched well with the ESA＇s （European Space Agency） results by using the same data,

Iterative Adaptive Median Filter for Impulse Noise Cancellation

基于推动噪音的特征，作者建立一个新过滤器，到图象的 charactics 的反复的适应中部的过滤器( IAMF ) .According 由推动噪音弄脏，他们证实重量 ftnction 与反复的算法结合了消除 noises.In IAMF 过滤器过程，因为噪音点不参予计算，他们不在图象影响正常的点，因此， IAMF 能保留详细很好，在处理图象，和 simult

Stochastic Iterative Learning Control With Faded Signals

Stochastic iterative learning control(ILC) is designed for solving the tracking problem of stochastic linear systems through fading channels. Consequently, the signals used in learning control algorithms are faded in the sense that a random variable is multiplied by the original signal. To achieve the tracking objective, a two-dimensional Kalman filtering method is used in this study to derive a learning gain matrix varying along both time and iteration axes. The learning gain matrix minimizes the trace of input error covariance. The asymptotic convergence of the generated input sequence to the desired input value is strictly proved in the mean-square sense. Both output and input fading are accounted for separately in turn, followed by a general formulation that both input and output fading coexists.Illustrative examples are provided to verify the effectiveness of the proposed schemes.

Generalized cubature quadrature Kalman filters:derivations and extensions

A new Gaussian approximation nonlinear filter called generalized cubature quadrature Kalman filter (GCQKF) is introduced for nonlinear dynamic systems. Based on standard GCQKF, two extensions are developed, namely square root generalized cubature quadrature Kalman filter (SR-GCQKF) and iterated generalized cubature quadrature Kalman filter (I-GCQKF). In SR-GCQKF, the QR decomposition is exploited to alter the Cholesky decomposition and both predicted and filtered error covariances have been propagated in square root format to make sure the numerical stability. In I-GCQKF, the measurement update step is executed iteratively to make full use of the latest measurement and a new terminal criterion is adopted to guarantee the increase of likelihood. Detailed numerical experiments demonstrate the superior performance on both tracking stability and estimation accuracy of I-GCQKF and SR-GCQKF compared with GCQKF.

Particle filter based on iterated importance density function and parallel resampling

The design, analysis and parallel implementation of particle filter(PF) were investigated. Firstly, to tackle the particle degeneracy problem in the PF, an iterated importance density function(IIDF) was proposed, where a new term associating with the current measurement information(CMI) was introduced into the expression of the sampled particles. Through the repeated use of the least squares estimate, the CMI can be integrated into the sampling stage in an iterative manner, conducing to the greatly improved sampling quality. By running the IIDF, an iterated PF(IPF) can be obtained. Subsequently, a parallel resampling(PR) was proposed for the purpose of parallel implementation of IPF, whose main idea was the same as systematic resampling(SR) but performed differently. The PR directly used the integral part of the product of the particle weight and particle number as the number of times that a particle was replicated, and it simultaneously eliminated the particles with the smallest weights, which are the two key differences from the SR. The detailed implementation procedures on the graphics processing unit of IPF based on the PR were presented at last. The performance of the IPF, PR and their parallel implementations are illustrated via one-dimensional numerical simulation and practical application of passive radar target tracking.

PIC-MF-based Iterative Receiver for Multiuser Space Frequency Block Coding Systems

A novel low-complexity iterative receiver for multiuser space frequency block coding (SFBC) system was proposed in this paper. Unlike the conventional linear minimum mean square error (MMSE) detector, which requires matrix inversion at each iteration, the soft-in soft-out (SISO) detector is simply a parallel interference cancellation (PIC)-matched filter (MF) operation. The probability density function (PDF) of PIC-MF detector output is approximated as Gaussian, whose variance is calculated with a priori information fed back from the channel decoder. With this approximation, the log likelihood ratios (LLRs) of transmitted bits are under-estimated. Then the LLRs are multiplied by a constant factor to achieve a performance gain. The constant factor is optimized according to extrinsic information transfer (EXIT) chart of the SISO detector. Simulation results show that the proposed iterative receiver can significantly improve the system performance and converge to the matched filter bound (MFB) with low computational complexity at high signal-to-noise ratios (SNRs).

Iterative Learning Controller Design for CNC Machine Tools

The repetitive processing and large quantity of single product represented by 3C products are urgently needed.However,for current processing operations,previous processing data have not been used in the optimization of control input.In order to utilize previous processing data to facilitate the next process and avoid adverse effects caused by repetitive disturbance and noise,the idea of iterative learning was introduced to improve the accuracy of machining.On the control level,since it is difficult to obtain high accuracy by traditional feedback control when faced with complex trajectories,an open⁃loop iterative learning controller and a position loop feedback controller were introduced,which worked fast with good convergence effects.Aiming at reducing the influence of accidental error,step type iterative learning was put forward.The iteration mechanism was stopped when the accuracy converged to the allowable range so as to reduce computational complexity,store the current iterative part of the control input,and make constant value compensation.However,in simulation and experiment,it was found that after superposition of the iterative learning controller,the phenomenon of partial divergence of the system tracking error occurred.Therefore,the speed and acceleration characteristics of input trajectories in time domain and frequency domain were analyzed.High⁃frequency noise was introduced in frequency domain,which was found to be the cause of the abovementioned phenomenon,and high⁃frequency components were filtered to solve the problem.To further improve the accuracy of convergence and avoid filtering effective high⁃frequency information in some area,a switchable filter based on the analysis of the frequency characteristics of input trajectory was proposed.Through SIMULINK simulation and dSPACE experimental verification,it was proved that the iterative learning controller of modifying controlled quantity and filter based iterative learning control method are effective.

Probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update

Aiming at improving the observation uncertainty caused by limited accuracy of sensors,and the uncertainty of observation source in clutters,through the dynamic combination of ensemble Kalman filter(EnKF) and probabilistic data association(PDA),a novel probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update is proposed.Firstly,combining with the advantages of data assimilation handling observation uncertainty in EnKF,an observation iterated update strategy is used to realize optimization of EnKF in structure.And the object is to further improve state estimation precision of nonlinear system.Secondly,the above algorithm is introduced to the framework of PDA,and the object is to increase reliability and stability of candidate echo acknowledgement.In addition,in order to decrease computation complexity in the combination of improved EnKF and PDA,the maximum observation iterated update mechanism is applied to the iteration of PDA.Finally,simulation results verify the feasibility and effectiveness of the proposed algorithm by a typical target tracking scene in clutters.

滤波辨识(12):多变量OEARMA系统的滤波辅助模型递阶广义增广迭代参数辨识

针对多变量输出误差自回归滑动平均(M-OEARMA)系统,即多变量Box-Jenkins系统,利用滤波辨识理念和辅助模型辨识思想,研究和提出了滤波辅助模型递阶广义增广梯度迭代辨识方法、滤波辅助模型递阶多新息广义增广梯度迭代辨识方法、滤波辅助模型递阶递推广义增广最小二乘迭代辨识方法、滤波辅助模型递阶多新息广义增广最小二乘迭代辨识方法等。这些滤波辅助模型递阶广义增广迭代辨识方法可以推广到其它有色噪声干扰下的线性和非线性多变量随机系统中。

滤波辨识(11):多变量CARARMA系统的滤波递阶广义增广迭代参数辨识

针对多变量受控自回归自回归滑动平均(M-CARARMA)系统,利用滤波辨识理念和递阶辨识原理,研究和提出了滤波递阶广义增广梯度迭代辨识方法、滤波递阶多新息广义增广梯度迭代辨识方法、滤波递阶递推广义增广最小二乘迭代辨识方法、滤波递阶多新息广义增广最小二乘迭代辨识方法等。这些滤波递阶广义增广迭代辨识方法可以推广到其它有色噪声干扰下的线性和非线性多变量随机系统中。

基于迭代卡尔曼滤波器的GPS-激光-IMU融合建图算法

在当前机器人导航和环境感知领域,室外大尺度场景下的三维激光SLAM一直是一个挑战性问题。由于GPS信号在某些环境下的不稳定性和激光SLAM的误差累积特性,传统算法在大尺度场景下表现不佳。针对室外大尺度场景下三维激光SLAM(同步定位和地图构建)存在的误差累积严重问题,本文提出了一种基于迭代卡尔曼滤波器的GPS-激光-IMU融合建图算法。该算法通过利用惯性测量单元(IMU)数据对机器人状态进行预测,同时以激光和全球定位系统(GPS)数据作为观测,更新机器人状态,推导出观测方程和雅可比矩阵,显著提高了建图的精度和鲁棒性。里程计中融合GPS数据的绝对位置信息以解决长时间运行中的误差累积问题。在特征稀疏的环境中,由于约束不足可能导致算法崩溃,GPS数据的引入可以提高系统的鲁棒性。此外,重力对于IMU数据预测机器人状态起到关键的作用。虽然重力是三维向量,但在不发生区域变化的情况下,其模长是不变的,因此被视为二自由度向量。通过将重力的优化转化为旋转矩阵群上的优化,成功避免了重力过参数化的问题,提高了算法的精度。在室外场景下与其他算法进行了性能测试对比并且验证了在大尺度场景下的鲁棒性和精度,结果表明:本文算法的均方根误差为0.089 m,与其他算法相比降低了54%。

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。