基于EKF的异步电机转速和负载转矩估计

合理选择电机的容量具有重要的意义,电机的容量可根据电机的转速和负载转矩确定,将电机的转速和负载转矩同时作为系统的状态,提出了一种基于EKF同时估计异步电机转速和负载转矩的方法,建立了包含异步电机转速和负载转矩状态的系统模型,基于该模型用EKF实现了同时估计异步电机转速和负载转矩,仿真和实验验证了所提方法能以较高的精度同时估计出电机的转速和负载转矩.     

Modeling of a hydraulic engine mount for active pneumatic engine vibration control using the extended Kalman filter

The attenuation of engine vibration transmitted to a chassis has been a major focus in the automotive community for the increase of comfort for the driver and passengers. A hydro-mount system is designed to reduce the transmission of engine vibration to the chassis. It is also used for supporting the static load by an engine weight. In this paper, we present a modeling and parameter estimation of hydro-mount systems. Nonlinear model aspects are developed and used with experimental data to validate the model response characteristics. These parameters will be modeled as a variable vector and its value is estimated via linearized and extended Kalman filter. This approach can help engineers reduce design time by providing insight into the effects of various parameters within the hydro-mount. Based on the estimated parameters, the simulation result confirmed that the derived passive model describes the dynamic behavior of the hydro-mount system accurately. This paper was recommended for publication in revised form by Associate Editor Shuzhi Sam Ge Arjon Turnip received his B.S. and M.S. degrees in Engineering Physics from the Institute of Technology Bandung, Indonesia, in 1998 and 2003, respectively. He is currently a Ph.D. program student in the School of Mechanical Engineering, Pusan National University, Korea. His research areas are integrated vehicle control, adaptive control, and estimation theory. Keum-Shik Hong received the B.S. degree in mechanical design and production engineering from Seoul National University in 1979, the M.S. degree in ME from Columbia University in 1987, and both the M.S. degree in applied mathematics and the Ph.D. degree in ME from the University of Illinois at Urbana-Champaign in 1991. He served as an Associate Editor for Automatica (2000–2006) and as an Editor for the International Journal of Control, Automation, and Systems (2003–2005). Dr. Hong received Fumio Harashima Mechatronics Award in 2003 and the Korean Government Presidential Award in 2007. Dr. Hong’s research interests include nonlinear systems theory, adaptive control, distributed parameter system control, robotics, and vehicle controls. Seonghun Park received his B.S. and M.S. degrees in mechanical engineering from KAIST in 1994 and 1996, respectively, and his Ph.D. degree from Columbia University in 2005. Dr. Park is currently a professor of mechanical engineering at Pusan National University, Korea. His research interests are in the areas of control, tribology, and biomechanics.     

基于扩展卡尔曼滤波的液压马达故障诊断

针对液压马达在实践中经常出现的故障模式,从液压马达的模型出发,抽取重点关心的状态方程,采用扩展卡尔曼滤波(EKF)器的状态估计方法,对液压马达故障进行诊断研究,较好地提高了故障诊断的效率与精度。经某型液压马达故障诊断仿真结果表明,该方法可以准确地对液压马达的故障进行诊断,是一种有效的故障诊断方法。     

Dual extended Kalman filter for combined estimation of vehicle state and road friction

Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute(HSRI) tire model; the dual extended Kalman filter (DEKF) technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions(high-friction, low-friction, and joint-friction). On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.     

自适应卡尔曼滤波器在火箭弹落点估计中的应用

利用雷达对火箭弹一段飞行过程中的参数进行量测,对火箭弹落点进行了准确估计,实现了火箭弹的轨迹修正。采用具有自适应调节滤波增益矩阵的卡尔曼滤波器,结合质点弹道模型,建立了自适应卡尔曼滤波弹道模型,完成了对三坐标雷达探测的一段火箭弹飞行参数的野值处理与滤波,并对火箭弹落点进行外推。数值仿真结果表明,经自适应调节的卡尔曼滤波器滤波后,弹道量测信号中的野值与噪声被有效去除,且滤波方差可以在短时间内收敛。根据滤波时间与落点估计误差的关系,采用滤波时间为8-10 s 方案,可得到最佳的落点估计。     

改进扩展卡尔曼滤波算法在智能足球机器人比赛中的应用

为了减少系统延时对高实时性机器人足球比赛的影响,在深入研究卡尔曼滤波算法原理的基础上,提出了一种改进的扩展卡尔曼滤波(EKF)算法,建立了足球机器人竞赛中小球的运动模型,通过仿真实验给出了小球运动状态的预测轨迹。实验结果表明,改进后的EKF算法可以有效地解决以往算法在高度非线性化区域的不稳定性等问题,同时改进后的算法提高了系统实时性,算法易于实现且预测效果较好。     

Robust extended Kalman filter of discrete-time Markovian jump nonlinear system under uncertain noise

This paper examines the problem of robust extended Kalman filter design for discrete-time Markovian jump nonlinear systems with noise uncertainty. Because of the existence of stochastic Markovian switching, the state and measurement equations of underlying system are subject to uncertain noise whose covariance matrices are time-varying or un-measurable instead of stationary. First, based on the expression of filtering performance deviation, admissible uncertainty of noise covariance matrix is given. Secondly, two forms of noise uncertainty are taken into account: Non-Structural and Structural. It is proved by applying game theory that this filter design is a robust mini-max filter. A numerical example shows the validity of the method.     

Measurement of synchronous machine parameters using Kalman filter based fuzzy logic estimator

This paper presents a new Kalman filter/fuzzy logic approach for estimating synchronous machine parameters from short circuit tests. The technique uses on-line noisy measurements of the short circuit current for estimating direct axis reactances, and time constant synchronous machine parameters. The approach is based on expressing short circuit current as a discrete time linear dynamic system model suitable for the Kalman filter to estimate the parameters. Fuzzy rule-based logic is used to tune-up measurement noise levels by adjusting the covariance matrix. The results show a better convergence using fuzzy logic than those solely using the Kalman filter.     

应用Kalman滤波器提高机抖激光陀螺姿态测量系统瞬时精度的方法

机械抖动激光陀螺输出的原始数据不仅包含了外界的惯性输入的角速率信息,还包含了抖动信号的角速率信息,通常使用线性相位FIR滤波器去除机械抖动信号,信号经滤波后其所有频率成分都将产生一定时间的延迟,这将对由机抖陀螺组成的高精度实时姿态测量系统造成影响.本文提出了一种基于姿态角运动跟踪预测模型和Kalman预测的姿态测量滤波延迟补偿方法.实验结果表明,本方法能有效估计并预测运动载体的姿态运动,补偿由于FIR滤波器时间延迟带来的不利影响,提高姿态测量系统的实时性和瞬时精度.     

实时电子稳像系统中Kalman滤波器的FPGA实现

针对实时电子稳像系统中Kalman滤波器的硬件实现问题,提出了一种Kalman滤波的FPGA实现方法。通过对电子稳像算法中Kalman滤波模型的分析,对滤波算法进行了化简,将滤波器的运算分解成简单的加、减、乘、除运算。利用硬件描述语言对Kalman滤波进行了FPGA实现,并对实现的滤波器进行了验证。通过软硬件仿真实验结果的对比,验证了所实现滤波器的有效性。     

基于离散Kalman滤波的桥梁结构振动信息处理技术研究

为获得较高精度的桥梁结构振动状态信息,提出先采用离散Kalman滤波对桥梁结构振动监测信息进行预处理,再用分布式Kalman动态数据融合算法进行融合。仿真结果表明该方法有效可行。     

双卡尔曼滤波算法在锂电池SOC估算中的应用

以在线估计锂离子电池组的荷电状态(SOC)为目的,建立了双卡尔曼滤波(DEKF)算法.以Thevenin电池模型和卡尔曼滤波算法为基础,对电池模型建立了状态空间表达式.分别采用最小二乘法和DEKF算法对该模型参数进行辨识,提高了该模型的精度,使电池模型能够较好地反映电池内部的真实状态.介绍了双卡尔曼滤波算法在线估算荷电状态的原理,并设计了相关的电池测试实验.实验结果表明在不同的工况环境下,该算法在线估计SOC具有较高的精度和对环境的适应度,最大误差小于4.5％.最后,验证了DEKF算法具有较好的收敛性和鲁棒性,可以有效解决初值估算不准和累积误差的问题.     

Implementation of a Kalman filter in positioning for autonomous vehicles, and its sensitivity to the process parameters

Modern autonomous vehicles are using more than one method for performing the positioning task. The most common positioning methods for indoor vehicles are odometry for relative positioning and triangulation for absolute positioning. In many cases a Kalman filter is required to merge the data from the positioning systems and determines the vehicle position based on error analysis of the measurements and calculation procedures. A Kalman filter is particularly advantageous for on-the-fly positioning, which is performed while the vehicle is in motion. This paper presents the implementation of a Kalman filter in ROBI — an AGV for material handling in a manufacturing environment. The performance of the filter in estimating the position of the AGV and the effect of motion parameters (speed, path curvature, beacon layout etc.) on filter accuracy are shown.     

基于FPAA与序贯双卡尔曼滤波信息融合的PCR温控

传统PCR(polymerase chain reaction)仪对样品反应池壁温度进行接触式测量与温控,存在测量迟滞大、无法直接控制试剂温度的缺点。研制的电化学实时定量PCR温控系统采用红外温度传感器、热源温度传感器以及环境温度传感器分别测量试剂表面热辐射、热源与反应池壁温度与环境温度,并提出序贯双卡尔曼滤波估计算法对三点温度数据进行信息融合从而估计出试剂表面温度真实值。该算法中迭代卡尔曼滤波器(iterated extended Kalman filter,IEKF)与线性卡尔曼滤波器(Kalman filter,KF)顺序运行,结合了IEKF非线性估计收敛快与KF实时性高的优点,克服了红外测温噪声大,易受环境、被测物热辐射率等因素影响的缺点。试剂温度的估计值作为反馈输入到基于FPAA(field programmable analog array)的可动态配置PID控制器中构成闭环控制,同时微控制器根据不同温控阶段的控制要求对PID控制器进行配置,从而提高温控效率。实验表明,滤波估计后红外测温精度由2℃提高至0.3℃;试剂表面温度监控与可动态配置PID使得PCR温控更加准确高效;PCR产物测试结果优于市面上PCR仪、恒温时间设置更加合理。     

状态χ2检验法在惯性航姿系统内阻尼卡尔曼滤波器中的应用

本文探讨了如何利用惯性测量组合本身的信息来提高捷联航姿系统的姿态精度。根据平台式阻尼网络的思想，设计了捷联式内阻尼卡尔曼滤波器，将惯导系统捷联解算获得的姿态与加速度计估计的姿态进行组合，在系统非加速度状态下，提高了姿态输出的精度。为了实时监测系统的运动状态从而判断内阻尼姿态的有效性，本文成功将状态χ2检验法应用在内阻尼卡尔曼滤波器中，设计了基于2个状态传播器的故障监测器，并通过对故障检测向量元素的检验代替对整个向量的检验，提高了故障监测的灵敏度和可靠性。最后，实际系统的动静态实验验证了本文所提出的方法的有效性。     

Automatic detection and analysis of cell motility in phase‐contrast time‐lapse images using a combination of maximally stable extremal regions and Kalman filter approaches

Phase‐contrast illumination is simple and most commonly used microscopic method to observe nonstained living cells. Automatic cell segmentation and motion analysis provide tools to analyze single cell motility in large cell populations. However, the challenge is to find a sophisticated method that is sufficiently accurate to generate reliable results, robust to function under the wide range of illumination conditions encountered in phase‐contrast microscopy, and also computationally light for efficient analysis of large number of cells and image frames. To develop better automatic tools for analysis of low magnification phase‐contrast images in time‐lapse cell migration movies, we investigated the performance of cell segmentation method that is based on the intrinsic properties of maximally stable extremal regions (MSER). MSER was found to be reliable and effective in a wide range of experimental conditions. When compared to the commonly used segmentation approaches, MSER required negligible preoptimization steps thus dramatically reducing the computation time. To analyze cell migration characteristics in time‐lapse movies, the MSER‐based automatic cell detection was accompanied by a Kalman filter multiobject tracker that efficiently tracked individual cells even in confluent cell populations. This allowed quantitative cell motion analysis resulting in accurate measurements of the migration magnitude and direction of individual cells, as well as characteristics of collective migration of cell groups. Our results demonstrate that MSER accompanied by temporal data association is a powerful tool for accurate and reliable analysis of the dynamic behaviour of cells in phase‐contrast image sequences. These techniques tolerate varying and nonoptimal imaging conditions and due to their relatively light computational requirements they should help to resolve problems in computationally demanding and often time‐consuming large‐scale dynamical analysis of cultured cells.     

Software sensor for on-line estimation of the microbial activity in activated sludge systems

This paper considers the design of a software sensor (or soft-sensor) for the on-line estimation of the biological activities of a colony of aerobic micro-organisms acting on activated sludge processes, where the carbonaceous waste degradation and nitrification processes are taken into account. These bioactivities are intimately related to the dissolved oxygen concentration. Two factors that affect the dynamics of the dissolved oxygen are the respiration rate or the oxygen uptake rate (OUR) and the oxygen transfer function (K(l)a). These items are challenging topics for the application of recursive identification due the nonlinear characteristic of the oxygen transfer function, and to the time-varying feature of the respiration rate. In this work, OUR and the oxygen transfer function are estimated through a software sensor, which is based on a modified version of the discrete extended Kalman filter. Numerical simulations are carried out in a predenitrifying activated sludge process benchmark and the obtained results demonstrate the applicability and efficiency of the proposed methodology, which should provide a valuable tool to supervise and control activated sludge processes.     

Pico satellite attitude estimation via Robust Unscented Kalman Filter in the presence of measurement faults

In the normal operation conditions of a pico satellite, a conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into consideration with a small weight, and the estimations are corrected without affecting the characteristics of the accurate ones. Two different RUKF algorithms, one with single scale factor and one with multiple scale factors, are proposed and applied for the attitude estimation process of a pico satellite. The results of these algorithms are compared for different types of measurement faults in different estimation scenarios and recommendations about their applications are given.     

Analysis of the optimal sampling rate for state estimation in sensor networks with delays

When addressing the problem of state estimation in sensor networks, the effects of communications on estimator performance are often neglected. High accuracy requires a high sampling rate, but this leads to higher channel load and longer delays, which in turn worsens estimation performance. This paper studies the problem of determining the optimal sampling rate for state estimation in sensor networks from a theoretical perspective that takes into account traffic generation, a model of network behaviour and the effect of delays. Some theoretical results about Riccati and Lyapunov equations applied to sampled systems are derived, and a solution was obtained for the ideal case of perfect sensor information. This result is also interesting for non-ideal sensors, as in some cases it works as an upper bound of the optimisation solution.