基于ELM和MCSCKF的锂离子电池SOC估计

为了减少噪声对锂离子电池荷电状态估计的影响,本文提出一种新颖的基于极限学习机和最大相关熵平方根容积卡尔曼滤波的SOC估计方法。首先,利用泛化性好、运行速度快的极限学习机作为卡尔曼滤波的测量方程;其次,基于灰狼优化算法,极限学习机的超参数被优化以提高电池荷电状态的估计精度;最后,基于最大相关熵平方根容积卡尔曼滤波,极限学习机的测量噪声被进一步减弱。所提方法可以简化极限学习机繁琐的调参过程,且为闭环的SOC估计方法。所提方法在多工况和宽温度范围内被测试以验证其泛化性能。测试结果显示,所提方法明显地提高了锂离子电池的荷电状态估计精度。同时,对比其他算法,所提方法的平均运行时间仅仅为长短时序列和循环门控单元网络的三分之一。当行驶工况复杂、温度变化区间较大时,所提方法的均方根误差小于1%,最大误差小于3%。当存在初始误差与环境噪声时,所提方法显示出了优越的鲁棒性。      

鲁棒UKF滤波算法在SINS初始对准中的应用

针对系统存在不确定性扰动时传统UKF滤波算法的滤波精度和鲁棒性均下降的问题,提出了一种基于H∞范数的鲁棒UKF滤波算法.该算法在Krein空间内对简化UKF滤波算法进行改进,增加了一个鲁棒环节.鲁棒环节通过引入给定正常数调整滤波增益从而提高滤波算法的鲁棒性能.在SINS大方位失准角初始对准中对简化UKF滤波算法和鲁棒UKF滤波算法进行了对比研究.仿真结果表明:与简化UKF滤波算法相比,鲁棒UKF滤波算法的方位失准角估计误差由16.9'缩小到4.3'.鲁棒UKF滤波算法降低了系统对扰动的敏感度,具有更好的滤波性能.     

基于快速SR-UKF的锂离子动力电池SOC联合估计

针对标准无迹卡尔曼滤波(Unscented Kalman filter, UKF) 算法本身存在着因状态误差协方差矩阵无法实现Cholesky分解而导致滤波发散的隐患,以及在电池状态估计过程中由离线标定的电池等效模型参数而造成的累积误差的问题,本文发展了一种平方根无迹卡尔曼滤波(Square-root unscented Kalman filter, SR-UKF)算法,并设计了一种电池状态联合估计策略。首先快速SR-UKF算法通过对观测方程进行准线性化处理,降低了每次无迹变换时的计算开销;然后在迭代过程中,用状态误差协方差矩阵的平方根代替状态误差协方差矩阵,该平方根是由QR分解与 Cholesky因子的一阶更新得到,解决了UKF 算法迭代过程中可能由计算累积误差引起状态误差协方差矩阵负定而导致滤波结果发散的问题,保证了电池荷电状态(State of charge,SOC)在线滚动估计的数值稳定性;最后采用联合估计策略,对电池等效模型参数进行实时辨识,保证了电池等效模型的准确性与有效性,从而提高了电池SOC的估计精度。仿真对比结果验证了快速SR-UKF算法以及电池状态联合估计策略的可行性与鲁棒性。      

A model-based algorithm for blood glucose control in type I diabetic patients

A model-based predictive control algorithm is developed to maintain normoglycemia in the Type I diabetic patient using a closed-loop insulin infusion pump. Utilizing compartmental modeling techniques, a fundamental model of the diabetic patient is constructed. The resulting nineteenth-order nonlinear pharmacokinetic-pharmacodynamic representation is used in controller synthesis. Linear identification of an input-output model from noisy patient data is performed by filtering the impulse-response coefficients via projection onto the Laguerre basis. A linear model predictive controller is developed using the identified step response model. Controller performance for unmeasured disturbance rejection (50 g oral glucose tolerance test) is examined. Glucose setpoint tracking performance is improved by designing a second controller which substitutes a more detailed internal model including state-estimation and a Kalman filter for the input-output representation. The state-estimating controller maintains glucose within 15 mg/dl of the setpoint in the presence of measurement noise. Under noise-free conditions, the model-based predictive controller using state estimation outperforms an internal model controller from literature (49.4% reduction in undershoot and 45.7% reduction in settling time). These results demonstrate the potential use of predictive algorithms for blood glucose control in an insulin infusion pump.     

Dynamic Optimization of Feedforward Automatic Gauge Control Based on Extended Kalman Filter

Automatic gauge control is an essentially nonlinear process varying with time delay,and stochastically varying input and process noise always influence the target gauge control accuracy.To improve the control capability of feedforward automatic gauge control,Kalman filter was employed to filter the noise signal transferred from one stand to another.The linearized matrix that the Kalman filter algorithm needed was concluded;thus,the feedforward automatic gauge control architecture was dynamically optimized.The theoretical analyses and simulation show that the proposed algorithm is reasonable and effective.     

陡脉冲干扰下的心电信号滤波及QRS提取

为消除陡脉冲带来的干扰,分析了陡脉冲干扰的特点,建立了陡脉冲噪声数学模型,提出了基于变分模态分解（Variational mode decomposition, VMD）的心电信号滤波算法,提取叠加在心电信号中陡脉冲干扰分量、识别陡脉冲干扰分量并剔除陡脉冲干扰分量;为减少VMD分解层数、提高实时性并减少内存消耗,提出了心电信号预处理算法;针对医疗环境中的随机噪声伴随陡脉冲出现的情况,分析了VMD后子信号中随机噪声的特点,提出了基于VMD子信号能量估计的阈值去噪算法;利用变分模态分解的带通滤波器组特性,提出了基于变分模态分解子信号重组的QRS波群检测算法,配合滤波算法以提高心电信号特征检测精度。以添加了高斯白噪声和模拟陡脉冲干扰的MIT?BIH数据库心电信号和医疗环境中采集的心电信号为实验对象,分别实现对滤波算法和QRS波群检测算法的定量对比分析。      

General Realization Algorithm for Modal Identification of Linear Dynamic Systems

The general realization algorithm (GRA) is developed to identify modal parameters of linear multi-degree-of-freedom dynamic systems subjected to measured (known) arbitrary dynamic loading from known initial conditions. The GRA extends the well known eigensystem realization algorithm (ERA) based on Hankel matrix decomposition by allowing an arbitrary input signal in the realization algorithm. This generalization is obtained by performing a weighted Hankel matrix decomposition, where the weighting is determined by the loading. The state-space matrices are identified in a two-step procedure that includes a state reconstruction followed by a least-squares optimization to get the minimum prediction error for the response. The statistical properties (i.e., bias, variance, and robustness to added output noise introduced to model measurement noise and modeling errors) of the modal parameter estimators provided by the GRA are investigated through numerical simulation based on a benchmark problem with nonclassical damping.     

Application of distance-dependent resolution compensation and post-reconstruction filtering for myocardial SPECT

Compensation for distance-dependent resolution can be directly incorporated in maximum likelihood reconstruction. Our objective was to examine the effectiveness of this compensation using either the standard expectation maximization (EM) algorithm or an accelerated algorithm based on use of ordered subsets (OSEM). We also investigated the application of post-reconstruction filtering in combination with resolution compensation. Using the MCAT phantom, projections were simulated for 360 degrees data, including attenuation and distance-dependent resolution. Projection data were reconstructed using conventional EM and OSEM with subset size 2 and 4, with/without 3D compensation for detector response (CDR). Also post-reconstruction filtering (PRF) was performed using a 3D Butterworth filter of order 5 with various cutoff frequencies (0.2-1.2 cycles cm(-1)). Image quality and reconstruction accuracy were improved when CDR was included. Image noise was lower with CDR for a given iteration number. PRF with cutoff frequency greater than 0.6 cycles cm(-1) improved noise with no reduction in recovery coefficient for myocardium but the effect was less when CDR was incorporated in the reconstruction. CDR alone provided better results than use of PRF without CDR. Results suggest that using CDR without PRF, and stopping at a small number of iterations, may provide sufficiently good results for myocardial SPECT. Similar behaviour was demonstrated for OSEM.     

基于EEMD的轧辊偏心补偿控制

针对傅里叶变换、小波算法等传统信号处理方法在非线性信号的提取与重构中存在的缺陷,提出了基于聚合经验模态分解的轧辊偏心信号提取新方法。另外,针对传统自动厚度控制系统(AGC)在偏心补偿控制中的不足,设计了有偏心补偿环节的 AGC系统。新方法将轧制力信号分解为多个不同特征模态函数,从中提取表征偏心信号的特征模态函数,并用此重构偏心信号,最后将新方法重构的偏心信号投入到此系统中控制轧件厚度。仿真及实验结果表明,利用聚合经验模态分解方法重构得到的轧辊偏心模型可以很大程度减小厚度波动,补偿效果优于小波算法。     

A constrained neural network Kalman filter for price estimation in high frequency financial data

In this paper we present a neural network extended Kalman filter for modeling noisy financial time series. The neural network is employed to estimate the nonlinear dynamics of the extended Kalman filter. Conditions for the neural network weight matrix are provided to guarantee the stability of the filter. The extended Kalman filter presented is designed to filter three types of noise commonly observed in financial data: process noise, measurement noise, and arrival noise. The erratic arrival of data (arrival noise) results in the neural network predictions being iterated into the future. Constraining the neural network to have a fixed point at the origin produces better iterated predictions and more stable results. The performance of constrained and unconstrained neural networks within the extended Kalman filter is demonstrated on "Quote" tick data from the $/DM exchange rate (1993-1995).     

Aircraft Icing Detection, Identification, and Reconfigurable Control Based on Kalman Filtering and Neural Networks

Flight in all weather conditions has necessitated correctly detecting icing and taking reasonable measures against it. This work aims at the detection and identification of airframe icing based on statistical properties of aircraft dynamics and reconfigurable control protecting aircraft from hazardous icing conditions. A Kalman filter is used for the data collection for the detection of icing, which aerodynamically deteriorates flight performance. A neural network process is applied for the identification of icing model of the aircraft, which is represented by five parameters based on past experiments for iced wing airfoils. Icing is detected by a Kalman filtering innovation sequence approach. A neural network structure is embodied such that its inputs are the aircraft estimated measurements and its outputs are the parameters affected by ice, which corresponds to the aircraft inverse dynamic model. The necessary training and validation set for the neural network model of the iced aircraft are obtained from the simulations of nominal model, which are performed for various icing conditions. In order to decrease noise effects on the states and to increase training performance of the neural network, the estimated states by the Kalman filter are used. A suitable neural network model of aircraft inverse dynamics is obtained by using system identification methods and learning algorithms. This trained model is used as an application for the control of the aircraft that has lost its controllability due to icing. The method is applied to F16 military and A340 commercial aircraft models and the results seem to be good enough.     

The problem of estimation of the temperature state upon heating of articles of nonferrous metals

Estimation of the surface temperature of a continuous aluminum cylindrical blank has been simulated in the process of heating under conditions where the statistical characteristics of the object noise and measurement noise are known a priori. A linear model of an object with distributed parameters was replaced by a seven-order model with lumped parameters. It is shown that such a replacement under given conditions yields a satisfactory precision and the model retains the properties of controllability and observability. To construct an observer based on a Kalman filter, special functions of the MATLAB medium have been employed. It has been found that for a wide range of dispersions of the measurement noise and control noise the precision of the temperature estimates over the cross sections of the blank are quite satisfactory.     

Improving Precision in the Reference Velocity of ADCP Measurements Using a Kalman Filter with GPS and Bottom Track

Global positioning system (GPS) data are used to measure boat velocity during acoustic Doppler current profiler (ADCP) discharge measurements, particularly when bottom tracking (BT) is biased by moving bed. A Kalman filter is developed to improve the velocity reference used by the ADCP under such conditions. Kalman filtering is a recursive statistical technique that estimates the current state of a process, given various inputs and their variance. In the case of data obtained by ADCP, the availability of two independent velocity measurements and a position measurement makes this method particularly attractive. The new Kalman filter combines raw inputs for GPS position (GGA) and Doppler velocity (VTG) with BT data in real time to produce best estimates of velocity. The technique is evaluated and calibrated using various accuracies of GPS data collected simultaneously along with unbiased BT data at two different sites. On the Gatineau River, real-time kinematic and wide area augmentation system corrections were used for this study. On the Saint Mary’s River, nondifferential GPS was collected. To examine the conditions under which such a system would be required, synthetic data for a moving bed contamination of BT were created. In all moving bed conditions evaluated, the Kalman filter estimates of reference velocity were superior to raw inputs.     

Cyclic beamforming using fractional Fourier transform domain cyclostationarity

A minimum mean-squared error (MSE) beamforming algorithm employing the optimum frac- tional Fourier transform (Opt-FrFT) domain second-order cyclostationarity is proposed. This method can efficiently filter out the compact desired chirp signal, with a consequence that the cyclically un- correlated interferences and stationary (colored) Gaussian noise are greatly suppressed in the Opt- FrFT domain. This improves the MSE minimization cyclic beamformer by reducing effectively the Opt-FrFr domain signal-noise cross terms in the presence of finite data length de-correlation opera- tion. Simulation results show that the new method works well under a wide range of signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR).     

基于融合模型的锂离子电池荷电状态在线估计

针对锂离子电池荷电状态（Stage of charge,SOC）在线估计精度不高,等效电路模型法估计精度与模型复杂度相矛盾的问题,本文对扩展卡尔曼滤波算法进行了改进,并以电池工作电压、电流为输入,对应等效电路模型法的SOC估计误差为输出,采用极限学习机算法,建立基于输入输出数据的SOC估计误差预测模型,采用物理–数据融合方法,基于误差预测模型,建立了等效电路模型法结合极限学习机的锂离子电池SOC在线估计模型。仿真结果表明,改进扩展卡尔曼滤波算法提高了算法的估计精度,而物理–数据融合的锂离子电池SOC在线估计模型减小了由电压、电流测量所引入的估计误差,克服了等效电路模型法估计精度与模型复杂度之间相矛盾的问题,进一步提高了SOC的估计精度,满足估计误差不超过5%的应用需求。      

Vibration Control of Wind-Excited Tall Buildings Using Sliding Mode Fuzzy Control

A sliding mode fuzzy control (SMFC) is proposed to design a controller for the third-generation benchmark problem on wind-excited buildings. A distinctive feature in vibration control of large civil infrastructure is the existence of large disturbances, such as wind, earthquake, and sea wave forces. Those disturbances govern the behavior of the structure; however, they cannot be precisely measured, especially for the case of wind excitations. Since the structural accelerations are measured only at a limited number of locations without the measurement of the wind forces, the structure of the conventional control may have the feedback loop only. The general structure of the SMFC, proposed herein, is composed of a compensation part and a convergent part. The compensation part prevents the system from diverging, and the convergent part directs the system to the sliding surface. The compensation part uses not only the structural response measurement but also the disturbance measurement, so the SMFC has a feedback loop and a feedforward loop. To realize the virtual feedforward loop for the wind-induced vibration control, a disturbance estimation filter is introduced. The structure of the filter is constructed based on an autoregressive model for the stochastic wind force. This filter estimates the wind force at each time instance based on the measured structural responses and the stochastic information of the wind force. For verification of the proposed algorithm, numerical simulation is carried out on the benchmark problem for wind-excited buildings. The results indicate that the present control algorithm is efficient for reducing the wind-induced vibration.     

基于二级特征匹配的卡尔曼滤波跟踪算法

运动目标的检测和跟踪是计算机视觉领域的研究热点和难点。本文针对复杂的交通应用背景,提出了基于二级特征匹配的卡尔曼滤波跟踪算法。首先在背景建模中引入一个差异深度矩阵以抑制噪声影响,利用基于背景差分的目标检测确定目标区域,然后运用卡尔曼预测目标位置,并以目标区域的几何特征和核直方图特征对当前帧检测到的目标模板和预测区域内的目标进行二级特征匹配。实验表明,该算法能够在保证多运动目标跟踪的准确有效性的基础上达到实时性应用要求。     

Kalman Filtering with Regional Noise to Improve Accuracy of Contaminant Transport Models

Spatially independent Gaussian noise has been widely assumed in examining the Kalman filter (KF) properties in different areas of engineering practice. However, for subsurface modeling, it is more reasonable to consider both data and noise as regional. In this study, regional noises are employed in KF and finite-difference schemes in solving the subsurface transport problem. A KF is constructed as a data assimilation scheme for a subsurface numeric model. Also, a regional random field simulation scheme is proposed and employed to examine the impact on effectiveness of KF correction processes. The results indicate that the prediction error of the KF data assimilation scheme is 30% smaller than the error from the deterministic model. Furthermore, by applying a correct regional noise structure, the KF data assimilation scheme reduces the prediction error from 25 to 10 ppm in our model, indicating an improvement of 60% in prediction accuracy.     

板形测量辊输出信号处理方法的研究与应用

板形测量值是板形控制系统中的重要组成部分,它的准确性直接关系到板形控制的效果。研究了1780冷连轧机板形控制系统的板形检测原理及控制方法。通过对数据处理方法的分析,应用通道数确定、插值和滤波等方法对检测数据进行处理,能够有效地抑制板形检测数据中的干扰影响,提高了测量系统的抗干扰能力。用这种数据处理方法对实测板形值进行了处理,取得良好效果,充分说明这种数据处理方法是1种理想的板形数据处理方法。     

Blast furnace stockline measurement using radar

Abstract

This paper presents a synergistic approach to stockline depth tracking within a blast furnace. Frequency modulated continuous wave (FMCW) radar can be used to measure the depth and surface profile of the burden surface; however, the radar signal is easily disturbed by radar anomalies during the process of continuous measurement. Data from the rotating chute and the charging signal provide information on the contextual relevance of these anomalies. An improved Kalman filter and anomaly detection model were developed to increase measurement accuracy by utilising contextual information. The approach was validated on production blast furnaces. The root mean squared (RMS) error in the measured depth was reduced by 17% when the proposed approach is used. The results suggest that this approach successfully adapts to changes in the pattern and characteristics of the burden surface.