A comparison of least squares and maximum likelihood methods using sine fitting in ADC testing

ADC test methods require the best possible reconstruction of the input signal of the ADC under test from the acquired, therefore erroneous, ADC output data. The commonly used least squares (LS) fit and the recently introduced maximum likelihood (ML) estimation are competing methods. This paper presents a simulation-based comparative study of these estimation methods with the goal to investigate the behavior of both methods and to determine their limits. Two alternative algorithms for the calculation of the maximum likelihood fit are considered (gradient-based minimization and differential evolution). The main finding is that while for low-INL (linear) ADCs the two methods (LS and ML) give similar results, for practical (almost always nonlinear) ADCs ML is definitely better.     

Parameter estimation for a dual-rate system with time delay

This paper investigates the parameter estimation problem of the dual-rate system with time delay. The slow-rate model of the dual-rate system with time delay is derived by using the discretization technique. The parameters and states of the system are simultaneously estimated. The states are estimated by using the Kalman filter, and the parameters are estimated based on the stochastic gradient algorithm or the recursive least squares algorithm. When concerning state estimate of the dual-rate system with time delay, the state augmentation method is employed with lower computational load than that of the conventional one. Simulation examples and an experimental study are given to illustrate the proposed algorithm.     

地下管道泄漏检测定位中的时延估计方法

本文将一种新的自适应无偏时延估计方法用于地下管道的泄漏检测和漏点定位，避免了经典自适应时延估计算法-LMS算法在噪声环境下可能产生有偏时延估计的不足。文章为该方法建立了新的自适应准则，分析了由该准则得到的最优解，并提出相应的算法来实现最优解的搜索，也详细的推导了算法的收敛条件。实验表明，该方法能够有效地消除不相关噪声的干扰，使得估计性能得到显著的改善。     

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used.     

高速傅里叶红外光谱仪TurboFT的控制及最佳测量温度方法研究

本文介绍了美国D＆P公司生产的高速傅里叶红外光谱仪TurboFT,对该仪器的控制软件进行了二次开发,使其拥有了仪器参数设置,采集光谱,黑体定标等功能。并利用其自带黑体进行多组不同温度下的全波段采样,经过最小二乘拟合法计算出各波段的温度与光谱值的响应函数,利用原光谱数据计算出理论温度值,并与实际温度值进行比较,筛选出均方差最小的前二十组数据,经过统计,总结出在不同光谱叠加数下,温度测试光谱最佳曲线拟合的波段范围,为今后使用该仪器进行物体表面温度测量提供了最佳测量依据。并利用所得温度曲线进行了四组对不同岩石的表面温度测量实验,验证了该方法的可行性。这项研究完成为今后在使用该仪器进行矿物分析的过程中,增添了物体表面温度预测的辅助功能。     

Robust state estimation for a class of uncertain nonlinear systems: Comparison of two approaches

In this paper, two approaches for robust state estimation of a class of Lipschitz nonlinear systems are proposed. First, a novel Unknown Input Observer (UIO) is designed without observer matching condition satisfaction. Then, an $H_{\infty }$ observer for approximate disturbance decoupling is proposed. Sufficient conditions for the existence of both proposed observers are derived based on a Lyapunov function. The achieved conditions are formulated in terms of a set of linear matrix inequalities (LMIs) and optimal gain matrices are obtained. The minimum values of the disturbance attenuation levels for both methods are obtained through solving optimization problems. Finally, the proposed approaches are compared by simulation studies of an automated highway system.     

单矢量水听器的几种DOA估计方法

针对单矢量水听器,将旋转不变技术(estimation of signal parameters via rotational invariance techniques,ESPRIT)等参数估计算法用于直方图统计,并提出了基于单矢量水听器的多重信号分类(multiple signal classification,MUSIC)算法,推导出波束形成的功率谱公式和半功率波束宽度公式以及不同声压振速组合情形下定向性能的理论下界,同时也对其他波达方向(direc-tion of arrival,DOA)估计方法进行了总结。理论分析和实验结果表明:ESPRIT等用于直方图统计后能够获取更为全面准确的目标信息;MUSIC算法可获得比最小方差无畸变响应(minimum variance distortionless response,MVDR)更高的分辨率,而且同样满足实时处理要求;MVDR的半功率波束宽度与信噪比大致呈现反比例关系,而常规波束形成(conventional beam-forming,CBF)的半功率波束宽度收敛于131.06°,两者都只与信噪比有关;声能流法等适用于较高信噪比情形,而波束形成和子空间方法的定向精度在较低信噪比情形下仍能接近理论下界,尤其是波束形成具有良好的宽容性;功率谱估计的3种算法具有相同的估计误差边界;各定向算法的时间花销都较小,均可应用于实时系统。     

Nested DE based parameter estimation for multiple vortex ring microburst model

Modelling of microburst is significant for flight simulations. The most widely used model of microburst is the one based on the principle of multiple vortex ring. The characteristics of the microburst are directly determined by the parameters of the model. The conventional method to fix the parameters of multiple vortex ring microburst model is using experiential formula, which is not flexible enough. In this paper, we treat the parameters estimation of multiple vortex ring model as an optimization problem, and introduce the differential evolution algorithm to solve it. In the process of estimating the parameters by the maximum wind velocity, there are two interrelated optimization processes. Therefore, we improved the standard differential evolution algorithm in this paper. A nested differential evolution algorithm is proposed to accomplish the two optimization process, intermediate optimization and objective optimization. The numerical experiments show that this method can flexibly generate microburst with any maximum wind velocity, and the error meet the requirement set by the user.     

散焦和运动模糊复合模型及参数估计

为了解决散焦和运动模糊同时存在的图像复原问题,建立了一种散焦和运动模糊复合模型,提出了一种基于自相关的参数估计方法.在分析图像成像过程的基础上,对标准散焦模型和运动模糊模型进行卷积,估计复合模型的分布范围,根据光的散射特性和能量守恒调整复合模糊模型的幅度.对复合模型按两方向分别求导,根据导数矩阵自相关的曲线特性估计模糊参数.实验结果表明,参数估计方法具有较高的估计精度,解决了混叠现象造成的干扰问题,复合模型则能够取得较好的复原效果.     

Force estimation and failure detection based on disturbance observer for an ear surgical device

The disturbance observer is one of the useful tools for estimating the contact force between the subject body and the environment in robotic and mechatronic systems. This paper introduced a novel automatic office-based ear surgical device for the treatment of Otitis Media with Effusion (OME) under the guidance of force sensing information. Since the force sensing information must be reliable so as to ensure the safety of the device, a contact force estimation method based on the disturbance observer is proposed. The system model is built and a control strategy is proposed and developed. In the control strategy, a composite motion controller for an ultrasonic motor (USM) stage is presented, and then the design and the stability analysis of an advanced disturbance observer is given. Furthermore, a contact estimator and a failure detector, aiming to enhance the safety and reliability enhancement, are designed. Finally, the proposed control strategy is studied with both simulation and experiment. The experimental results show that the advanced disturbance observer can estimate the actual contact force correctly and precisely, and the disturbance observer based force estimation and failure detection method is feasible which can be used in force sensing, contact detection and fault diagnosis.     

基于肌肉协同激活模型的上肢关节运动连续估计

关节运动连续估计为基于表面肌电信号的人机交互提供了一种更为自然灵活的方式。提出了一种基于肌肉协同理论和支持向量回归的激活模型进行上肢关节角度的估计。首先利用非负矩阵分解算法对肌电信号进行解耦,提取独立动作的协同元;然后根据非负最小二乘算法计算相应协同元激活系数;最后通过支持向量回归构建了映射激活系数到关节角度的激活模型,利用建立的激活模型从采集的表面肌电信号得到关节运动的连续估计。对2个关节独立和组合运动的估计实验表明,该模型能获得较高的估计精度。     

A thinning algorithm for GPS-based unbiased FIR estimation of a clock TIE model

A thinning algorithm is proposed for real-time unbiased finite impulse response (FIR) estimation of the local clock time interval error (TIE) model (time error, fractional frequency offset, linear frequency drift rate, etc.) employing GPS-based sawtooth measurements. We show that the approach allows obtaining practically optimal estimates of the clock states, by large horizons (number of the points in the average). The algorithm is applied to the TIE measurements allowing for different time steps and averaging horizons for each of the clock states and compared to the three state Kalman filter. It is demonstrated that, in the presence of the sawtooth noise induced by the GPS receiver, the unbiased FIR estimates with thinning out fit the clock states better than the Kalman filter, in terms of the Allan deviation and precision time protocol deviation.     

Robust control design for a wheel loader using and feedback linearization based methods

The heavy equipment industry is building more and more equipment with electro-hydraulic control systems. The existing industry practices for the design of control systems in construction machines primarily rely on classical designs coupled with ad-hoc synthesis procedures. Such practices produce desirable results, but lack a systematic procedure to account for invariably present plant uncertainties in the design process as well as coupled dynamics of the multi-input multi-output (MIMO) configuration. In this paper, two H_∞ based robust control designs are presented for an automatic bucket leveling mechanism of a wheel loader. In one case, the controller is designed for the base plant model. In another case, the controller is designed for the plant with a feedback linearization control law applied yielding improved stability robustness. A MIMO nonlinear model for an electro-hydraulically actuated wheel loader linkage is considered. The robustness of the controller designs are validated by using analysis and by simulation using a complete nonlinear model of the wheel loader linkage and hydraulic system.     

采用椭圆算法和Canny算子的超声TOF估计方法

椭圆算法被用于测量超声回波信号的渡越时间(TOF),以便估计目标的距离.然而,椭圆算法是将估计出的椭圆长轴位置对应的时间点作为TOF值,估计的目标距离值与实际距离值仍有一定误差.为了进一步提高椭圆算法的TOF估计精确度,将Canny边缘检测引入到椭圆算法中,结合回波脉冲包络的特征形成一种新的超声TOF估计方法.首先利用边缘检测算子提取出近似椭圆形的回波脉冲包络的边界,然后根据边界上点对应的信号幅度用差分方法搜索除起始点以外的梯度最大值位置对应的时间确定为TOF值.实验结果表明:改进后的椭圆算法测量绝对误差可以控制在1.5 mm以内,较传统方法相比至少提高0.5 mm.而相对误差达到0.2％,较传统方法提高了30％.改进后的椭圆算法的应用使得测量的准确性和可靠性均得到了明显提升.     

改进卡尔曼滤波的融合型锂离子电池SOC估计方法

荷电状态(SOC)估计对于锂离子电池充放电优化控制、任务规划、可靠性提升等均具有重要价值,针对广泛应用的卡尔曼滤波(KF)一类方法存在的参数设置无具体标准、模型性能随工况环境改变而适应性降低等问题,提出一种噪声方差可变卡尔曼滤波方法(VVKF)的SOC估计算法,该算法每次迭代时估计并设定最适应当前系统状态的的噪声方差,克服了KF噪声方差初值依靠人为经验设定而造成精度下降的问题,同时采用最小二乘支持向量机作为KF的量测方程,通过建立样本库的方式克服电池型号以及工况改变对SOC估计精度的影响。采用马里兰大学CACLE中心锂离子电池数据集的实验证明了VVKF较KF性能的提升以及SOC估计的有效性。     

Reliability estimation for cutting tools based on logistic regression model using vibration signals

As an important part of CNC machine, the reliability of cutting tools influences the whole manufacturing effectiveness and stability of equipment. The present study proposes a novel reliability estimation approach to the cutting tools based on logistic regression model by using vibration signals. The operation condition information of the CNC machine is incorporated into reliability analysis to reflect the product time-varying characteristics. The proposed approach is superior to other degradation estimation methods in that it does not necessitate any assumption about degradation paths and probability density functions of condition parameters. The three steps of new reliability estimation approach for cutting tools are as follows. First, on-line vibration signals of cutting tools are measured during the manufacturing process. Second, wavelet packet (WP) transform is employed to decompose the original signals and correlation analysis is employed to find out the feature frequency bands which indicate tool wear. Third, correlation analysis is also used to select the salient feature parameters which are composed of feature band energy, energy entropy and time-domain features. Finally, reliability estimation is carried out based on logistic regression model. The approach has been validated on a NC lathe. Under different failure threshold, the reliability and failure time of the cutting tools are all estimated accurately. The positive results show the plausibility and effectiveness of the proposed approach, which can facilitate machine performance and reliability estimation.     

永磁直驱风电系统建模及其机电暂态模型参数辨识

针对基于双脉宽调制(PWM)变换器的永磁直驱风电系统的运行特性,分析了风力机特性、电机侧变换器和电网侧变换器的控制策略,利用Matlab/Simulink建立了反映电力电子开关动作的永磁直驱风电系统详细模型,并在此基础上根据同步电机3阶暂态模型,建立了直驱风机的机电暂态数学模型,采用粒子群算法(PSO)对模型进行了参数辨识。仿真结果表明,该详细模型能够描述永磁直驱风电系统对不同风速的响应,实现风能的最大功率跟踪;机电暂态数学模型与详细模型特性接近,能够从总体上反映永磁直驱风电系统对端电压变化的有功、无功响应,PSO参数辨识有效。研究结果表明,所建立的详细模型能够用于控制方式的研究以改善输出特性,机电暂态模型能够用于研究电网与永磁直驱风电系统的相互影响。     

管道泄漏检测中自适应时间延迟估计方法的设计

介绍了用自适应时间延迟估计方法设计管道泄漏检测系统的过程,详细阐述了其工作原理、自适应时间延迟估计方法及最小均方(LMS)自适应时间延迟估计算法,简要说明了其工作特点。     

An intelligent estimation method for product design time

The planning and control of product development is based on the pre-estimation of product design time (PDT). In order to optimize the product development process (PDP), it is necessary for managers and designers to evaluate design time/effort at the early stage of product development. However, in systemic analytical methods for PDT this is somewhat lacking. This paper explores an intelligent method to evaluate the PDT regarding this problem. At the early development stage, designers lack sufficient product information and have difficulty in determining PDT via subjective evaluation. Thus, a fuzzy measurable house of quality (FM-HOQ) model is proposed to provide measurable engineering information. Quality function deployment (QFD) is combined with a mapping pattern of “function→principle→structure” to extract product characteristics from customer demands. Then, a fuzzy neural network (FNN) model is built to fuse data and realize the estimation of PDT, which makes use of fuzzy comprehensive evaluation to simplify structure. In a word, the whole estimation method consists of four steps: time factors identification, product characteristics extraction by QFD and function mapping pattern, FNN learning, and PDT estimation. Finally, to illustrate the procedure of the estimation method, the case of injection mold design is studied. The results of experiments show that the intelligent estimation method is feasible and effective. This paper is developed to provide designers with PDT information to help them in optimizing PDP.     

An online two-stage adaptive algorithm for strain profile estimation from noisy and abruptly changing BOTDR data and application to underground mines

Strain measurement using BOTDR (Brillouin Optical Time-Domain Reflectometry) is nowadays a standard tool for structural health monitoring. In this context, weak data quality and noise, usually owed to defective fiber installation, hinders discriminating actual level shifts from outliers and might entail a biased risk assessment. We propose a novel online adaptive algorithm for strain profile estimation in strain time series with abrupt and gradual changes and missing data. It relies on a convolution filter in Brillouin spectrum domain and a smoothing technique in time domain. In simulated data, the convolution filter is shown to reduce strain measurement uncertainty by up to 8 times the strain resolution. The two-stage method is illustrated with systematic outliers removal from real data of a Chilean copper mine and the improvement of the associated gain spectrum quality by up to 18 dB in SNR terms.