基于预测补偿模型的绳索并联机构预松弛控制

本文分析了含绳长误差的绳索并联机构运动控制过程,设计了一种基于长短期记忆(LSTM)神经网络预测误差补偿模型的预松弛控制方法,提高了绳索并联机构末端位姿在运动过程中的准确性与连续性。本文基于矢量闭环原理建立了系统运动学模型,得到了绳索末端位姿的非线性误差模型,采用LSTM神经网络进行非线性误差的预测补偿。基于离散控制周期分配主、从控制绳,实现系统的预松弛控制,减小绳索末端的无序晃动。仿真结果表明,误差补偿之后,末端位姿精度有了明显的提高,而预松弛控制的位姿与索力相较于传统控制也更加连续,验证了该方法的可行性。     

基于极大似然估计的惯性测量组合标定方法

针对测试设备或平台框架转角误差( ASE)较大的情况,提出了一种用于惯性测量组合(IMU)标定的新方法.该方法采用相互正交的惯性器件输出的平方和作为观测信息,并以重力加速度ga和地速ωie的幅值平方作为参考基准来标定工具误差系数,这样能够消除ASE对标定结果的影响.同时针对误差项系数也较大的情况,给出了一种基于极大似然...     

基于PSO优化神经网络和空间网格的机器人位姿标定方法

该文提出一种将机器人的位置和姿态拆分开,分别进行标定的机器人位姿标定方法。采用空间精度控制网格标定机器人定位误差,粒子群优化算法(particle swarm optimization,PSO)优化神经网络标定机器人定姿误差。该方法以指数积公式(product of exponentials,POE)为基础建立机器人正向运动学模型,用映射法建立空间网格,用三坐标测量臂测量机器人位姿,用空间网格精度标定定位误差,用PSO优化的神经网络标定定姿误差。其优点在于既标定机器人工具中心点(TCP)的定位误差,又标定机器人工具坐标系的姿态误差,使得机器人定位、定姿误差都得到补偿。实验结果表明机器人的定位、定姿均方根误差减小接近一个数量级。     

微型航姿系统中三轴MEMS加速度计组合误差建模方法

根据微机械(MEMS)加速度计的测鲢原理对其误差源进行了分析,并研究了微型航姿系统中加速度计的非正交零偏以及温度特性.基于加速度计输出电压随温度变化的规律,利用分温度段对加速度计进行六位置标定的方法建立加速度计的误差模型,并应用于微型航姿系统,实现加速度计的实时补偿.多次实验结果表明,加速度计的误差模型校正了加速度计的...     

3-RPS对称并联支撑结构误差灵敏度研究

针对以3-RPS并联机构为核心的光电跟踪系统支撑结构,运用矩阵全微分理论,建立了基于Rodrigues参数的机构位姿误差模型.针对机构可达空间内各误差来源,提出了一种通过归一化描述灵敏度的新方法,并建立基于数学统计意义的灵敏度系数数学模型.通过此模型的数值仿真,可知在机构设计、加工以及装配过程中,对机构驱动误差与定平台上轴向误差需严格控制;当定、动平台结构比例系数改变时,其对整个运动空间各误差灵敏度系数百分比改变不大,即高灵敏度的误差源仍需严格控制.根据各误差的灵敏度系数可对其标定进行修正,达到有效减小误差灵敏度系数较高的运动学参数误差的目的,实现空中目标轨迹的实时跟踪瞄准.     

精密小型Hexapod并联机器人标定实验及精度分析

为了提高精密小型Hexapod并联机器人的运行精度,对机器人进行了标定实验及精度分析.推导了Hexapod机器人结构参数的误差模型、设计了标定步骤和算法,并在三坐标测量机上对机器人进行标定实验;从机构角度对Hexapod机器人的间隙误差来源进行了分析,并推导了间隙误差对机器人位姿误差的映射关系数学模型;推导并分析了计算过程中最小二乘误差、牛顿-拉普森迭代误差的数学模型,分析了机器人结构参数的辨识精度.标定实验结果表明：经过误差补偿,机器人位姿坐标的最大位移误差由0.267 6 mm降为0.010 5 mm;最大转角误差由0.006 8 rad降为0.001 1 rad.Hexapod机器人标定及精度分析方法对于开发精密型并联机器人具有参考价值.     

惯性导航系统中加速度计标定方案设计与研究

本文主要基于车载筒装导弹基础之上,对捷联惯性导航系统的加速度计标定方法和误差机理进行了研究。首先,对惯性导航系统加速度计误差机理进行分析,提出了加速度计的标定补偿方案,以车载作为激励方式,设计了不开箱加速度计标定方法,该方法能够对误差进行补偿来提高关系导航系统的精度。     

Study on error sensibility of 4DOF UPU parallel manipulators based on probability distribution

以具有3平动1转动的4自由度UPU机构为研究对象,分析了机构对影响其自由度特性及运动误差的各类扭角误差的敏感程度.首先,建立了机构的运动学模型,给出了可能发生的各类误差分类.以给定一定概率分布的数值误差为条件,应用数值方法对位姿正解进行了两种情况的计算和分析,即计算和分析了在误差不变的情况下机构末端操作器的位姿误差随时间的变化,以及扭角误差按照一定概率分布的前提下机构末端操作器在某一运动瞬时的位姿误差.通过分析和对比得到的数值结果和模拟图形,得出了各类扭角误差对机构运动平台影响的不同结果,按照其敏感程度,给出了源误差的大小.     

并联机床的动力学特性对加工精度影响的分析

为了提高并联机床的加工精度,分析了并联机床的动力学特性对加工精度的影响。根据牛顿—欧拉方程,得到并联机床的动力学方程,解得连杆的驱动力;根据杆件轴向伸长量与受力之间的关系,得到连杆的长度误差;以无长度误差的连杆长度为优化目标,用优化的方法,得到动平台的位姿,并与连杆有长度误差时动平台的位姿比较,得动平台的位姿误差;根据刀具在动平台坐标系中位置,得刀具加工位置误差及对被加工零件精度的影响。结果表明：并联机床连杆的长度误差,引起刀具加工位置误差,使被加工零件产生形位误差和尺寸误差;并联机床电主轴偏心引起连杆的长度误差的扰动,产生刀具加工位置的扰动误差,影响被加工零件的表面粗糙度。     

基于遗传算法的包装机械臂位置精度控制方法

目的为提高包装机械臂运行精度,解决视觉伺服控制系统中手眼标定问题,基于遗传算法设计一种机械臂运动学参数标定方法。方法在明确手眼视觉坐标的基础上,给出不同坐标系之间的变换关系。通过对比机械臂末端执行器理论位置和实际位置,确定其运动学参数标定误差模型。基于遗传算法基本原理,搭建了相关适应度函数,根据计算所得误差补偿量更新末端执行器位姿。最后,对机械臂运动控制系统进行联合调试以及实验分析。结果实际位置和理论位置之间偏差绝对值的平均值大约为0.8 mm,偏差最大值只有1.2 mm,精度比较高。结论所述手眼标定方法可以显著提高机械臂运动精度,可满足相关包装行业要求。     

Prediction of Drug Content and Hardness of Intact Tablets Using Artificial Neural Network and Near-Infrared Spectroscopy

The purpose of this study was to predict drug content and hardness of intact tablets using artificial neural networks (ANN) and near-infrared spectroscopy (NIRS). Tablets for the drug content study were compressed from mixtures of Avicel® PH-101, 0.5% magnesium stearate, and varying concentrations (0%, 1%, 2%, 5%, 10%, 20%, and 40% w/w) of theophylline. Tablets for the hardness study were compressed from mixtures of Avicel PH-101 and 0.5% magnesium stearate at varying compression forces ranging from 0.4 to 1 ton. An Intact Analyzer™ was used to obtain near infrared spectra from the tablets with varying drug contents, whereas a Rapid Content Analyzer™ (RCA) was used to obtain spectral data from the tablets with varying hardness. Two sets of tablets from each batch (i.e., tablets with varying drug content and hardness) were randomly selected. One set of tablets was used to generate appropriate calibration models, while the other set was used as the unknown (test) set. A total of 10 ANN calibration models (5 each with 10 and 160 inputs at appropriate wavelengths) and five separate 4-factor partial least squares (PLS) calibration models were generated to predict drug contents of the test tablets from the spectral data. For the prediction of tablet hardness, two ANN calibration models (one each with 10 and 160 inputs) and two 4-factor PLS calibration models were generated and used to predict the hardness of test tablets. The PLS calibration models were generated using Vision® software. Prediction of drug contents of test tablets using the ANN calibration models generated with 10 inputs was significantly better than the prediction obtained with the ANN calibration models with 160 inputs. For tablets with low drug concentrations (less than or equal to 2%w/w), prediction of drug content was better with either of the two ANN calibration models than with the PLS calibration models. However, prediction of drug contents of tablets with greater than or equal to 5% w/w drug was better with the PLS calibration models than with the ANN calibration models. Prediction of tablet hardness was better with the ANN calibration models generated with either 10 or 160 inputs than with the PLS calibration models. This work demonstrated that a well-trained ANN model is a powerful alternative technique for analysis of NIRS data. Moreover, the technique could be used in instances when the conventional modeling of data does not work adequately.     

Applicability of multilayer feed-forward neural networks to model the onset of river breakup

Predicting the onset of breakup is an essential component of any ice jam flood forecasting system, yet it presents a difficult challenge due to the complex nature of the relationship between meteorological conditions, streamflow hydraulics and ice mechanics. For this research, data extracted from historical hydrometric and meteorological records were used to develop and assess a three-layer feed-forward artificial neural network (ANN) model for predicting the onset of breakup, using the Hay River in northern Canada as the demonstration site. The calibration results illustrate the potential of the ANN model for successful forecasting of the onset of river ice breakup, i.e. the first transverse cracking of the ice cover. However, rigorous validation also indicates that the accuracy of such ANN models can be optimistically overestimated by their performance during the calibration phase. The possible reasons for this poor predictive capability of the ANN model are also discussed. Despite this caveat, the proposed model shows improved performance as compared to the more conventional multiple linear regression (MLR) techniques typically applied to this problem.     

Partitioning and interpolation based hybrid ARIMA–ANN model for time series forecasting

Time series data (TSD) originating from different applications have dissimilar characteristics. Hence for prediction of TSD, diversified varieties of prediction models exist. In many applications, hybrid models provide more accurate predictions than individual models. One such hybrid model, namely auto regressive integrated moving average – artificial neural network (ARIMA–ANN) is devised in many different ways in the literature. However, the prediction accuracy of hybrid ARIMA–ANN model can be further improved by devising suitable processing techniques. In this paper, a hybrid ARIMA–ANN model is proposed, which combines the concepts of the recently developed moving average (MA) filter based hybrid ARIMA–ANN model, with a processing technique involving a partitioning–interpolation (PI) step. The improved prediction accuracy of the proposed PI based hybrid ARIMA–ANN model is justified using a simulation experiment. Further, on different experimental TSD like sunspots TSD and electricity price TSD, the proposed hybrid model is applied along with four existing state-of-the-art models and it is found that the proposed model outperforms all the others, and hence is a promising model for TSD prediction.     

In-process monitoring of drilling burr formation using acoustic emission and a wavelet-based artificial neural network

Prediction/detection of exit burrs is critical in manufacturing automation. In this research, an intelligent burr sensing/monitoring scheme is proposed. Acoustic emission (AE) was selected to detect burr formation during drilling. For effective extraction of information contained in the collected AE signals, wavelet transform (WT) was adopted and the selected features through WT were fed into a back-propagation artificial neural net (ANN) as input vectors. To validate the in-process AE monitoring system, both WT-based ANN and cutting condition-based ANN outputs (cutting speed, feed, drill diameter, etc.) were compared with experimental data. The results show that the proposed scheme is not only efficient with fewer inputs, but more reliable in predicting drilling burr types over cutting condition-based ANN.     

Quantification of ternary mixtures of heavy metal cations from metallochromic absorbance spectra using neural network inversion

A new method based on artificial neural networks (ANN) for the processing of spectrophotometric data is proposed and illustrated on the example of the simultaneous quantification of ternary mixtures of zinc, cadmium, and mercury cations in aqueous solutions. Three types of commercially available metallochromic indicators were used as a simple model setup to create spectral data analogous to those normally received from an optical sensor array. In conventional ANN training methods for chemical sensors based on spectrophotometric data, a calibration is established by mathematically correlating the measured optical signal as network input with the concentration of the calibration sample as network output. In several situations, however, especially when dealing with mixed sample solutions, the relationship between a measured absorption spectrum and the corresponding ion concentrations is ambiguous, resulting in an "ill-posed problem". On the other hand, if the training direction is reversed by correlating known sample concentrations with measured optical signals, the relationship becomes reasonable for the ANN to obtain its structure. The proposed model illustrated in this paper is based on a more reasonable direct mapping and estimation by artificial neural network inversion (ANNI). In the training step, sample mixtures of known concentrations are optically measured to construct networks correlating the input data (ion concentrations) and the output data (absorption spectra). In the estimation step, the ion concentrations of unknown samples are estimated using the constructed ANN. The measured spectra of the unknown samples are fed to the output layer, and the appropriate input concentrations are determined by ANNI. When training the ANN system with 143 ternary mixtures of Zn2+, Cd2+, and Hg2+ in a concentration range from 1 to 100 microM, root-mean-square errors of prediction (RMSEP) of 0.45 (Zn2+), 0.96 (Cd2+), and 0.32 microM (Hg2+) were observed for the estimation of concentrations in 30 test samples, using the ANNI procedure. This newly proposed model, which involves the construction of an ANN based on direct mapping and estimation by ANNI, opens up one way to overcome the limitations of nonselective sensors, allowing the use of more easily accessible semiselective receptors to realize smart chemical sensing systems.     

Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction

Six popular approaches of «NIR spectrum–property» calibration model building are compared in this work on the basis of a gasoline spectral data. These approaches are: multiple linear regression (MLR), principal component regression (PCR), linear partial least squares regression (PLS), polynomial partial least squares regression (Poly-PLS), spline partial least squares regression (Spline-PLS) and artificial neural networks (ANN). The best preprocessing technique is found for each method. Optimal calibration parameters (number of principal components, ANN structure, etc.) are also found. Accuracy, computational complexity and application simplicity of different methods are compared on an example of prediction of six important gasoline properties (density and fractional composition). Errors of calibration using different approaches are found. An advantage of neural network approach to solution of «NIR spectrum–gasoline property» problem is illustrated. An effective model for gasoline properties prediction based on NIR data is built.     

绳系并联支撑机构的绳迟滞效应及影响实验研究

绳系并联机构因具有结构简单、工作空间大、惯性小等优势,得到了广泛的应用。该文针对支撑绳索中普遍存在的迟滞效应进行了实验研究。首先,分析了某绳系并联机器人（Wire-driven Parallel Robot,WDPR）样机中的绳迟滞曲线的数学模型,并利用实验数据识别出了模型中的相关参数;其次,通过不同条件下的绳索伸-缩实验,探讨了绳拉力迟滞效应的影响因素;接着,分析了迟滞效应对飞行器模型位姿和气动载荷参数解算的影响情况。最后,以某风洞试验模型支撑绳系并联机器人样机中支撑飞机模型的牵引绳的迟滞现象为例,对绳迟滞效应的影响情况进行了分析。研究结果表明:绳迟滞效应对模型位姿和利用绳拉力解算风洞试验的模型气动载荷都有一定的影响;绳索的材质与迟滞现象关系密切;预紧力对迟滞效应的影响程度具有决定性的作用,当绳预紧力增大到一定程度时,迟滞效应的影响是可以忽略的。     

Construction of a predictive model for concentration of nickel and vanadium in vacuum residues of crude oils using artificial neural networks and LIBS

A predictive model to determine the concentration of nickel and vanadium in vacuum residues of Colombian crude oils using laser-induced breakdown spectroscopy (LIBS) and artificial neural networks (ANNs) with nodes distributed in multiple layers (multilayer perceptron) is presented. ANN inputs are intensity values in the vicinity of the emission lines 300.248, 301.200 and 305.081 nm of the Ni(I), and 309.310, 310.229, and 311.070 nm of the V(II). The effects of varying number of nodes and the initial weights and biases in the ANNs were systematically explored. Average relative error of calibration/prediction (REC/REP) and average relative standard deviation (RSD) metrics were used to evaluate the performance of the ANN in the prediction of concentrations of two elements studied here.     

Aerodynamic optimization of turbomachinery blades using evolutionary methods and ANN-based surrogate models

A fast, flexible, and robust simulation-based optimization scheme using an ANN-surrogate model was developed, implemented, and validated. The optimization method uses Genetic Algorithm (GA), which is coupled with an Artificial Neural Network (ANN) that uses a back propagation algorithm. The developed optimization scheme was successfully applied to single-point aerodynamic optimization of a transonic turbine stator and multi-point optimization of a NACA65 subsonic compressor rotor in two-dimensional flow, both were represented by 2D linear cascades. High fidelity CFD flow simulations, which solve the Reynolds-Averaged Navier-Stokes equations, were used in generating the data base used in building the ANN low fidelity model. The optimization objective is a weighted sum of the performance objectives and is penalized with the constraints; it was constructed so as to achieve a better aerodynamic performance at the design point or over the full operating range by reshaping the blade profile. The latter is represented using NURBS functions, whose coefficients are used as the design variables. Parallelizing the CFD flow simulations reduced the turn-around computation time at close to 100% efficiency. The ANN model was able to approximate the objective function rather accurately and to reduce the optimization computing time by ten folds. The chosen objective function and optimization methodology result in a significant and consistent improvement in blade performance.     

A novel application of a microaccelerometer for target classification

This paper presents a novel method of target classification by means of a microaccelerometer. Its principle is that the seismic signals from moving vehicle targets are detected by a microaccelerometer, and targets are automatically recognized by the advanced signal processing method. The detection system based on the microaccelerometer is small in size, light in weight, has low power consumption and low cost, and can work under severe circumstances for many different applications, such as battlefield surveillance, traffic monitoring, etc. In order to extract features of seismic signals stimulated by different vehicle targets and to recognize targets, seismic properties of typical vehicle targets are researched in this paper. A technique of artificial neural networks (ANNs) is applied to the recognition of seismic signals for vehicle targets. An improved back propagation (BP) algorithm and ANN architecture have been presented to improve learning speed and avoid local minimum points in error curve. The improved BP algorithm has been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that target seismic properties acquired are correct, ANN is effective to solve the problem of classification and recognition of moving vehicle targets, and the microaccelerometer can be used in vehicle target recognition.