硬件在环系统中电机参数辨识及状态估计研究

矢量控制电机中的敏感参数转子时间常数的实时辨识和电机状态实时估计是电机高性能运行的保证,首先讨论了转子时间常数参数辨识,其次讨论了滑模观测器并给出了观测器稳定性分析,通过观测器得到感应电机磁链状态估计值;将得到的转子磁链值用于MRAS状态估计,得到实时的电机转速.设计了硬件在环仿真系统验证电机状态估计方法及参数辨识方法;硬件在环试验系统包含感应电机,电气测功器以及实时仿真器等硬件,通过硬件在环实时试验得到了实时的电机状态估计及参数辨识结果,试验结果验证了参数辨识及状态估计方法的有效性和实时性.     

基于LabVIEW/WT3000的永磁电机参数实时测试系统

针对永磁电机实时运行过程中的特征参数测试需求,设计了基于虚拟仪器的永磁电机参数实时测试系统;分析了永磁电机定子磁链实时估计方法,探讨了影响磁链参数精度的定子绕组电阻测量值实时校正方法,设计了基于以太网实时数据传输的LabVIEW/WT3000高精度功率分析仪的数据采集系统,并通过实时采集的常规参数完成对定子磁链等特征参数的在线估计;该测试系统包含信号采集、数据处理、曲线拟合、图形化显示及数据存储等功能,具有测试功能丰富、人机界面自定义、易实现远程测试等特点;通过对额定电压为130V的30KW永磁同步电机的测试,验证了该系统良好的实用性和可扩展性。     

模糊自抗扰控制在永磁同步电机调速系统的应用

针对直接转矩调速系统中PID控制器参数鲁棒性差,调速过程中磁链和转矩脉动大的问题,设计了一种基于模糊的改进自抗扰转速控制器;自抗扰策略代替传统的PID控制方法,模糊规则对自抗扰控制算法进行简化,减少待整定参数;与传统的PID控制方法相比,模糊自抗扰控制能提高调速系统的误差估计补偿和抗干扰能力;对比仿真结果,模糊自抗扰控制响应速度快,明显降低了系统在调速过程中的磁链和转矩脉动,表明模糊自抗扰控制具有良好的控制性能,验证了该方法的有效性。     

基于链码检测的直线段检测方法

直线是图像的重要特征，直线参数是进行图像识别和直线段三维重建的重要基础数据。基于链码检测的直线段的检测方法分为4步：以边缘图像为基础进行链码检测；根据链码估计曲率，检测链码角点，并在角点处拆分链码；通过链码直方图检测直线链码；对直线链码进行直线参数估计，并根据连接准则，进行直线连接。实验证明，该方法可以对直线段进行有效的检测。     

刮板输送机永磁同步电动机无位置传感器控制

传统转子磁链观测器受直流扰动和谐波扰动的影响无法准确估计转子磁链,进而基于转子磁链的永磁同步电动机无位置传感器控制方法无法准确估计转子位置。针对该问题,提出了一种基于三阶广义积分磁链观测器的刮板输送机永磁同步电动机无位置传感器控制策略。通过引入三阶广义积分磁链观测器,能有效抑制高次谐波分量,稳态时可完全消除转子磁链中的直流分量,且对基波的幅值和相位没有任何影响,从而准确估计转子磁链,提高转子位置估计精度。仿真结果验证了该控制策略的正确性和有效性。     

感应电机高阶终端滑模磁链观测器的研究

提出了基于高阶非奇异终端滑模的感应电机转子磁链观测方法,用于实现感应电机的按转子磁链定向控制. 设计了非奇异终端滑模面及观测器的控制策略,利用所设计的控制策略推导出电机转子磁链信息. 为了抑制常规滑模存在的抖振现象,设计了定子电流观测器的高阶滑模控制律,可将控制信号直接用于电机转子磁链的估计. 较常规滑模观测器,所提方法具有较高的观测精度,并对电机参数变化具有良好的鲁棒性.仿真结果验证了方法的有效性.     

基于改进Bayesian-MCMC的突发水污染事件预测模型参数率定方法

预测模型是科学制定应急处置措施的基础.为快速准确地构建突发水污染事件预测模型,将预测模型参数的率定问题视为贝叶斯估计问题,并根据有限差分方法和贝叶斯推理得到参数的后验概率密度函数,再通过改进的Metropolis-Hastings抽样方法得到较为合理的参数值.以发生在某明渠段的突发水污染事件为例,分析讨论等容量控制非均匀流和非等容量控制非均匀流两种情景下不同观测噪声对参数率定值的影响,并与由贝叶斯-马尔科夫链蒙特卡罗方法得到的参数值和真实值进行对比.结果表明:改进Bayesian-MCMC方法在计算精度、适用性和抗噪声等方面优于贝叶斯-马尔科夫链蒙特卡罗方法,能较好地率定模型参数,并为构建突发水污染事件预测模型提供了新思路.     

基于递推最小二乘法加速度计信息辅助的磁力计标定方法

由于MEMS磁力计自标定过程无法实现与MEMS加速度计间未对准误差的估计,提出一种利用加速度计矢量作为辅助信息,对磁力计的误差参数及磁力计与惯性单元间的未对准误差参数的一步估计方法。首先.对加速度计进行标定处理;然后,利用加速度计和磁力计间点积不变的性质,构造误差参数模型;最后通过递推最小二乘法完成对误差参数的迭代求解。通过仿真与实验对本文提出方法进行了验证,结果表明,本文提出方法估计出的磁力计误差参数的误差在10-4量级,标定后的磁力计与加速度计所在的惯性单元间的旋转角稳定在苏州磁倾角47.5°附近,完成了误差参数的一步估计。且迭代计算效率较高,相比于最小二乘法计算速度,在5秒钟左右就完成了对误差参数的估计,更适用于现场标定。     

状态和参数联合估计方法及其在飞行试验中的应用

本文提出了一种有效的状态和参数的联合估计方法.针对参数估计结果有偏或发散的问 题,本文给出了一种参数向量可控性模型,并由此模型得到了噪声相关的一种状态和参数的估 计方法.运用状态和参数联合估计的新方法进行飞行状态和测量仪器的误差估计,仿真和实 际飞行数据处理的结果表明;本文提出的方法可以给出飞行状态和仪器误差估计的满意结果, 比普通推广Kalman滤波方法更有效.     

Sunday算法效率分析

针对Sunday算法的过程比较复杂,难以构建马尔可夫链的问题,提出一种新的根据算法的匹配次数差求平均效率的方法。首先选定初等算法作为效率分析的基准算法,使用马尔可夫链得出初等算法比较精确的平均效率估计公式;然后根据相应的概率公式计算出初等算法和Sunday算法匹配过程的差值;将两者结合,得出Sunday算法平均效率估计公式。实验结果表明,由此公式计算的估计值可以代表实际匹配次数的平均值。     

Automatic glottal inverse filtering with the Markov chain Monte Carlo method

This paper presents a new glottal inverse filtering (GIF) method that utilizes a Markov chain Monte Carlo (MCMC) algorithm. First, initial estimates of the vocal tract and glottal flow are evaluated by an existing GIF method, iterative adaptive inverse filtering (IAIF). Simultaneously, the initially estimated glottal flow is synthesized using the Rosenberg–Klatt (RK) model and filtered with the estimated vocal tract filter to create a synthetic speech frame. In the MCMC estimation process, the first few poles of the initial vocal tract model and the RK excitation parameter are refined in order to minimize the error between the synthetic and original speech signals in the time and frequency domain. MCMC approximates the posterior distribution of the parameters, and the final estimate of the vocal tract is found by averaging the parameter values of the Markov chain. Experiments with synthetic vowels produced by a physical modeling approach show that the MCMC-based GIF method gives more accurate results compared to two known reference methods.     

一类有输入噪声扰动的逆系统无偏参数辨识算法研究

对逆系统建模时,原系统的输出作为逆系统参数辨识时的输入.由于原系统输出存在测量噪声,且噪声方差未知,采用普通最小二乘法辨识,无法得到逆系统参数的一致无偏估计.为此,本文研究了一种有输入扰动的的逆系统无偏参数辨识算法,该算法先通过小波变换估计输入信号噪声的方差,再由估计得到的方差,通过偏差消除的递推最小_乘法,对逆系统的参数进行无偏辨识.该算法降低了对输入辨识信号为白噪声的要求,具有较强的实用性.由于采用递推运算,该算法也可以用于逆系统参数的在线辨识.最后,通过实验验证了该算法的有效性.     

Model predictive control using an extended ARMarkov model

The original ARMarkov identification method explicitly determines the first μ Markov parameters from plant input–output data and approximates the slower dynamics of the process by an ARX model structure. In this paper, the method is extended to include a disturbance model and an ARIMAX structure is used to approximate the slower dynamics. This extended ARMarkov model is then used to formulate a predictive controller. As the number of Markov parameters in the model varies from one to P (prediction horizon)+1, the controller changes from generalized predictive control (GPC) to dynamic matrix control (DMC). The advantages of the proposed ARM-MPC are the consistency of the Markov parameters estimated by the ARMarkov method, independent tuning of the controller for servo and regulatory responses and the ability to combine the characteristics of GPC and DMC. The theoretical results are illustrated through simulation examples.     

On the generalized q-Markov cover models for discrete-time systems

A new method for obtaining generalized q-Markov cover models for discrete-time SISO systems is proposed and is based on the inverse solution of the Lyapunov equation. The reduced order models not only match the Markov parameters and high-frequency power moments like the conventional q-Markov cover techniques but also match time moments and low-frequency power moments of the original system     

Subspace identification – a Markov parameter approach

Estimating observability matrices or state sequences is the central component of existing subspace identification methods. In this paper a different approach, in which Markov parameters are first estimated under general input excitation, is proposed. The prominent difference of this approach is that a three-block arrangement of data matrices is used. It is shown that one advantage of this approach over other subspace algorithms is that several unbiased estimating procedures can be carried out. One immediate application is to obtain balanced or nearly balanced models directly from the estimated Markov parameters. Another application is that with the estimated Markov parameters, consistently initialized Kalman filter state sequences can be obtained, from which the system matrices can be easily determined without bias. Performance of the proposed algorithms is investigated in two case studies which are based on real data taken from two industrial systems. The algorithms developed in this paper have been implemented and are publicly available.     

Convergent stochastic Expectation Maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation

Estimation in the deformable template model is a big challenge in image analysis. The issue is to estimate an atlas of a population. This atlas contains a template and the corresponding geometrical variability of the observed shapes. The goal is to propose an accurate estimation algorithm with low computational cost and with theoretical guaranties of relevance. This becomes very demanding when dealing with high dimensional data, which is particularly the case of medical images. The use of an optimized Monte Carlo Markov Chain method for a stochastic Expectation Maximization algorithm, is proposed to estimate the model parameters by maximizing the likelihood. A new Anisotropic Metropolis Adjusted Langevin Algorithm is used as transition in the MCMC method. First it is proven that this new sampler leads to a geometrically uniformly ergodic Markov chain. Furthermore, it is proven also that under mild conditions, the estimated parameters converge almost surely and are asymptotically Gaussian distributed. The methodology developed is then tested on handwritten digits and some 2D and 3D medical images for the deformable model estimation. More widely, the proposed algorithm can be used for a large range of models in many fields of applications such as pharmacology or genetic. The technical proofs are detailed in an appendix.¹     

Parameter estimation in Markov random field contextual models usinggeometric models of objects

We present a new scheme for the estimation of Markov random field line process parameters which uses geometric CAD models of the objects in the scene. The models are used to generate synthetic images of the objects from random view points. The edge maps computed from the synthesized images are used as training samples to estimate the line process parameters using a least squares method. We show that this parameter estimation method is useful for detecting edges in range as well as intensity edges. The main contributions of the paper are: 1) use of CAD models to obtain true edge labels which are otherwise not available; and 2) use of canonical Markov random field representation to reduce the number of parameters     

利用新特征空间的SAS图像目标分类算法

通过分析合成孔径声纳图像中不同目标统计特性参数间的差异,提出了一种利用新特征空间的SAS图像目标分类算法。该算法用马尔可夫随机场分割算法找到感兴趣区域,提取阴影的几何参数和目标的归一化中心矩,并且将目标、阴影、背景之间统计特性的分布参数之差与前两者构成新的特征空间。利用克一均值聚类算法对三类目标进行分类。合成孔径声纳湖试数据验证了算法的有效性。     

Markov random field texture models

We consider a texture to be a stochastic, possibly periodic, two-dimensional image field. A texture model is a mathematical procedure capable of producing and describing a textured image. We explore the use of Markov random fields as texture models. The binomial model, where each point in the texture has a binomial distribution with parameter controlled by its neighbors and ``number of tries' equal to the number of gray levels, was taken to be the basic model for the analysis. A method of generating samples from the binomial model is given, followed by a theoretical and practical analysis of the method's convergence. Examples show how the parameters of the Markov random field control the strength and direction of the clustering in the image. The power of the binomial model to produce blurry, sharp, line-like, and blob-like textures is demonstrated. Natural texture samples were digitized and their parameters were estimated under the Markov random field model. A hypothesis test was used for an objective assessment of goodness-of-fit under the Markov random field model. Overall, microtextures fit the model well. The estimated parameters of the natural textures were used as input to the generation procedure. The synthetic microtextures closely resembled their real counterparts, while the regular and inhomogeneous textures did not.     

Bayesian estimation for homogeneous and inhomogeneous Gaussianrandom fields

This paper investigates Bayesian estimation for Gaussian Markov random fields. In particular, a new class of compound model is proposed which describes the observed intensities using an inhomogeneous model and the degree of spatial variation described by a second random field. The coupled Markov random fields are used as prior distributions, and combined with Gaussian noise models to produce posterior distributions on which estimation is based. All model parameters are estimated, in a fully Bayesian setting, using the Metropolis-Hasting algorithm. The full posterior estimation procedures are illustrated and compared using various artificial examples. For these examples the inhomogeneous model performs very favorably when compared to the homogeneous model, allowing differential degrees of smoothing and varying local textures