Parameter estimation of lithium-ion batteries and noise reduction using an H∞ filter

Lithium-ion batteries are widely used in conventional hybrid vehicles and in some electrical devices. A lumped parameter model of lithium-ion battery is constructed and system parameters are identified by using the autoregressive moving average (ARMA) and a genetic algorithm (GA). The precise information of state-of-charge (SOC) and terminal voltage are required to prolong the battery life and to increase the battery performance, reliability, and economics. By assuming a priori knowledge of the process and measurement noise covariance values, Kalman filter or extended Kalman filter has been used to estimate the SOC and terminal voltage. However, the main drawbacks of the Kalman filter is to use correct a priori covariance values, otherwise, the estimation errors can be lager or even divergent. These estimation errors can be relaxed by using the H_∞ filter, which does not make any assumptions about the noise, and it minimizes the worst case estimation error. In this paper, H_∞ filter is used to estimate the SOC and terminal voltage. The H_∞ filter can reduce SOC estimation error, making it more reliable than using a priori process and measurement noise covariance values.     

PCI-9846高速数字化仪在电火花放电特性测试中的应用

针对电火花放电间隙状态变化特性的检测与分析等问题,利用凌华PCI-9846四通道高速数字化仪建立了测量系统,讨论了电火花加工放电间隙放电特性测量方案,测量了不同加工状态下放电间隙两端的电压波形和电流波形,分析了空载、正常火花放电、过渡电弧放电和短路等不同加工状态下的电压波形特征及其特征参数。研究结果表明,正常火花放电、过渡电弧放电和短路等不同加工状态下间隙电压的阈值范围不同,且与加工电流有关;在设计伺服控制器时,应根据电源电压、加工电流、工件材料等来动态修正电压阈值。     

采用自适应一致性UKF的分布式目标跟踪

由于传统分布式跟踪方法在先验噪声协方差与其实际值不相匹配时跟踪误差较大,提出了一种采用自适应一致性无迹卡尔曼滤波的分布式目标跟踪方法,该方法首先执行分布式UKF算法得到对当前移动目标状态的估计值,然后通过一个系统错误检测机制,确定是否需要对噪声协方差值进行更新。如需要,则根据当前获得的测量信息去估计当前噪声协方差,并联合该估计值和先前的噪声协方差值获得一个新的先验噪声协方差值。最后根据新获得的噪声协方差值对获得的目标状态估计值进行修正。实验结果表明该方法具有较好的准确性和鲁棒性：在噪声未知环境下,基于ACUKF的分布式跟踪方法相比于基于容积信息滤波和基于分布式无迹卡尔曼滤波的跟踪方法,最大跟踪误差值分别减少了49.93%和 51.46%;在目标过程噪声发生动态变化的情况下,提出的方法相比于上述两种传统跟踪方法,跟踪误差值分别减少了40.67%和40.06%。     

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.     

An EM algorithm for dynamic estimation of interfacial boundary in stratified flow of immiscible liquids using EIT

Estimation of interfacial boundary between two immiscible liquids in two-phase flows through pipe line provides information about the flow characteristics and thus can aid in design and monitoring of the flow process. The interfacial boundary can be represented in several ways, one such method is the front point approach. Front points describe the location and the shape of the interfacial boundary separating the immiscible liquids. During the flow process, due to fluctuations the interfacial boundary and so the front points which describe the boundary changes with time. The time-varying interfacial boundary can be estimated using dynamic inverse algorithms based on Kalman filter. However, algorithms based on Kalman filter require complete knowledge of model parameters (initial states, state transition matrix, and noise covariance matrices) for implementation. In processes involving complex flow pattern such as two-phase flows, it is difficult to represent the model parameters in a prior form. This uncertainty in model parameters causes suboptimal performance of the Kalman type filters. In this paper, we employ expectation maximization algorithm (EM) to estimate model parameters along with the interfacial boundary using electrical impedance tomography (EIT). The estimation of model parameters reduces the modeling uncertainty and thus results in improving the tracking of interfacial boundary. Numerical and experimental studies are performed to validate the performance of the proposed method.     

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input–output direct feed-through term

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input–output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection.     

基于ASRCKF算法的锂电池SOC估算

针对平方根容积卡尔曼滤波(SRCKF)估算SOC时需要准确获得系统状态及测量噪声协方差这一缺陷,将基于电池模型输出电压残差序列的协方差匹配思想引入平方根容积卡尔曼滤波,提出了自适应平方根容积卡尔曼滤波算法(ASRCKF)。以18650型锂电池为实验对象,建立了戴维南等效电路模型,采用递推最小二乘法辨识电池模型参数,最后,利用UDDS电池实验数据对ASRCFK算法进行了仿真。实验结果表明,传统的SRCKF算法估算SOC产生的均方根误差为3.41%;而提出的ASRCKF算法估算SOC产生的均方根误差仅为0.97%,与传统算法相比具有更高的精度,对噪声的适应能力更强。     

基于Sage窗的自适应Kalman滤波用于钟差预报研究

钟差预报是时间保持工作中的一项关键技术。Kalman算法作为一种最优预报算法,具有实时性的特点,在时间保持工作中得到了广泛的应用。但是由于经典Kalman算法需要准确确定模型随机误差和测量误差,否则状态估计会引入一定的误差,在原子时算法中表现为原子钟噪声和钟差测量噪声。原子钟的噪声参数值通常是通过Allan方差估计,若估计不够准确,Kalman预报将会出现误差。通过研究基于Sage窗的自适应Kalman预报算法,实时修正状态模型误差。利用自适应因子调整状态预测协方差阵有效降低了模型误差,提高了预报精度,最后通过两台氢原子钟和两台铯原子钟的实测数据验证了算法的有效性。     

基于AEKF的车辆质量与道路坡度实时估计

针对车辆在实际行驶过程中外界噪声的统计特性无法已知的问题,以车辆纵向动力学模型为基础,提出了自适应扩展卡尔曼滤波(adaptive extended Kalman filter,简称AEKF)的车辆质量及道路坡度估计算法。以动态估计车辆系统中的质量与坡度为研究对象,引入了旋转质量换算系数,建立车辆纵向动力学系统的状态空间模型,考虑了不同时刻的档位匹配与行驶特殊工况的处理。对系统状态方程进行离散化处理,得到系统状态方程与系统测量方程,在扩展卡尔曼滤波(extended Kalman filter,简称EKF)的基础上引入带遗忘因子的噪声统计估计器,通过AEKF对状态方程与测量方程实时更新,进行在线估计和校正噪声统计值,从而解决系统的噪声时变问题。本研究算法与EKF算法估计及实测结果的对比分析表明,本研究算法能够很好地对车辆质量和坡度信号进行有效滤波和估计,在短时间内逐渐收敛并逼近实测值,从而能够合理有效地检测车辆在行驶过程中的状态信息。     

Identifying Bearing Rotor-Dynamic Coefficients Using an Extended Kalman Filter

An extended Kalman filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor-dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter's performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor-bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates the cross-coupled damping coefficients were the least accurate.     

复杂交通流下基于卡尔曼滤波的多目标全生命周期状态估计

针对复杂行车环境下噪声干扰和车辆行车过程中状态变化导致交通场景中目标状态估计精度低的问题,以毫米波雷达 为检测传感器,提出涵盖参数初始化和在线更新的基于卡尔曼滤波的多目标全生命周期状态估计方法。 首先,建立交通流下多 目标运动状态的卡尔曼滤波状态估计模型;基于此,一方面提出基于数据驱动的卡尔曼滤波观测噪声协方差矩阵初始化的新方 法,另一方面采用变分贝叶斯方法对卡尔曼滤波参数进行在线更新,以此提高多目标状态估计精度;最后,在算法实现步骤的基 础上,利用实车数据开展测试验证工作。 实验结果表明,方法的目标状态估计均方误差为 0. 153,相较于传统卡尔曼滤波减小 了 36. 2% ,证明所提出方法对提升车辆感知精度的有效性。     

S-修正的自适应卡尔曼滤波与模糊卡尔曼滤波相结合的汽车状态估计算法

精确的汽车状态信息的获取是汽车动态控制系统正常工作的前提。建立了二自由度汽车动力学模型,提出了将S-修正的自适应卡尔曼滤波与模糊卡尔曼滤波相结合进行汽车关键状态估计的方法。模糊卡尔曼滤波利用所设计的模糊控制器通过实时监测信息实际方差与理论方差的比值,实现对时变量测噪声的协方差矩阵的实时在线估计,提高了算法在时变量测噪声情况下的鲁棒性;S-修正的自适应卡尔曼滤波算法基于滤波不发散理论推导得出实时修正因子S,进而对估计误差协方差矩阵直接加权。两种方法的结合在总体上提高了在汽车动力学系统过程噪声与量测噪声协方差矩阵不准确情况下算法的鲁棒性与估计精度,最后通过基于ADAMS的虚拟试验验证了该方法的有效性。     

Comparison of Linearized Kalman Filter and Extended Kalman Filter for Satellite Motion States Estimation

The performance of the conventional Kalman filter depends on process and measurement noise statistics given by the system model and measurements.The conventional Kalman filter is usually used for a linear system,but it should not be used for estimating the state of a nonlinear system such as a satellite motion because it is difficult to obtain the desired estimation results.The linearized Kalman filtering approach and the extended Kalman filtering approach have been proposed for a general nonlinear system.The equations of satellite motion are described.The satellite motion states are estimated,and the relevant estimation errors are calculated through the estimation algorithms of the both above mentioned approaches implemented in Matlab are estimated.The performances of the extended Kalman filter and the linearized Kalman filter are compared.The simulation results show that the extended Kalman filter is much better than the linearized Kalman filter at the aspect of estimation effect.     

Machine learning induction of a model for online parameter selection in EDM rough machining

In electrical discharge machining (EDM), appropriate average current in the gap has to be selected for the given machining surface in order to obtain the highest material removal rate at low electrode wear. Thus, rough machining parameters have to be selected according to the machining surface. In the case of sculptured features, the machining surface varies with the depth of machining. Hence, the machining parameters have to be selected on-line to obtain appropriate current density in the gap. In this paper, inductive machine learning is used to derive a model based on the voltage and current in the gap. The sufficient inputs to the model are only two discharge attributes extracted from the voltage signal in the gap. The model successfully selects between two machining parameter settings that obtain different average surface current in the gap. It requires only voltage signal acquisition during the machining process and a simple algorithm that is easy to implement on industrial machines.     

Spacecraft attitude determination using a decoupling filter

In this paper, an algorithm for real-time attitude estimation of spacecraft motion is investigated. For efficient computation, the decoupling filter presented in this paper is accomplished by a derived pseudo-measurement from the given measurement and the decoupled state in the original system. However, the proposed decoupling filter contains model errors due to coupling terms in the system. Therefore, we develope an attitude determination algorithm in which coupling terms are compensated through an error analysis. The attitude estimation algorithm using the state decoupling technique for real-time processing provides accurate attitude determination capability under a highly maneuvering dynamic environment, because the algorithm does not have any bias errors from a truncation, and the covariance of the estimator is compensated by nonlinear terms in the system. To verify the performance of the proposed algorithm vis-a-vis the EKF (extended Kalman filter), and the nonlinear filter, simulations have been performed by varying the initial values of the state and covariance, and measurement covariance. Results show that the proposed algorithm has consistently better performance than the EKF in all of the ranges of initial state values and covariance values of measurement, and it is as accurate as the nonlinear filter. However, the convergence speed of the nonlinear filter is faster than the proposed algorithm because of the pseudo-measurement model errors in the proposed algorithm. We show that the computational time of the proposed algorithm is improved by about 23% over the nonlinear filter.     

An adaptive navigation method for a small unmanned aerial rotorcraft under complex environment

Focusing on low navigation performance of small unmanned aerial rotorcraft under complex environment, a composite navigation method combined with adaptive Kalman filtering and radial basis function neural network prediction method is proposed to improve navigation performance during GPS outages. When the GPS signal is available, an adaptive Kalman filter based on covariance scaling is introduced to deal with the process noise in real time. Meanwhile, a radial basis function neural network is trained on line to construct the projection among input (output of the inertial measurement unit, attitude and GPS losing time) and output (position error and velocity error). During GPS outages, the radial basis function neural network can provide high performance error estimation for position and velocity to improve state information. Finally, a land vehicle test and a flight test have confirmed that the proposed method can improve the navigation performance largely under complex environment.     

Image reconstruction algorithm of electromagnetic tomography based on fractional Kalman filter

Aiming at the problem of low quality of image reconstruction of electromagnetic tomography (EMT), in this paper, an image reconstruction algorithm of EMT based on fractional Kalman filter (FKF) is proposed. Firstly, the principle of EMT and the principle of state equation of FKF are expound respectively. FKF is often used in the state estimation of nonlinear systems. There is a nonlinear relationship between the object field distribution and the sensor signal in the EMT. Therefore, according to this feature, FKF is applied to the image reconstruction algorithm of EMT. The image reconstruction process of EMT is regarded as the state estimation process of FKF, the normalized measurement voltage is taken as the observation value, and the sensitivity matrix is taken as the measurement matrix. To establish the nonlinear state estimation equation of the FKF and a priori estimation error covariance equation in the EMT, the gray value of image obtained by LBP is used as the initial value of the state estimation, a prior estimation state vector and a priori estimation error covariance matrix are obtained by prediction update, the Kalman filter gain and the posterior estimation error covariance matrix are obtained by the correction feedback process. After repeated iterations, the final state vector, i.e. reconstructed image of EMT is obtained. Finally, simulation experiments are carried out for seven different flow patterns. The results show that the image error and correlation coefficients of the reconstructed image of this algorithm are better than traditional algorithms such as LBP, Landweber, Kalman filter, and have better anti-noise effect than Kalman filter. Therefore, the image reconstruction algorithm of FKF is a new method and means to study the image reconstruction of EMT.     

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.     

压电自适应微细电火花加工技术

针对微细电火花加工技术的特点,开发、研制了新型的压电自适应微细电火花加工装置,介绍了该装置的结构和工作原理,分析了压电致动器的性能以及放电间隙与开路电压的关系,并利用该装置进行了试验研究。试验结果表明该加工技术可以实现放电间隙与放电状态的自适应调节,加工效率较高。