工业机器人动力学参数的改进遗传算法辨识

为了准确辨识出六自由度工业机器人的动力学参数,提出一种基于改进遗传算法的参数辨识方法。构建牛顿-欧拉机器人动力学模型,明确反映各关节力矩与动力学参数的函数关系;通过改进遗传算法获取优化激励轨迹,并对机器人进行动力学参数的整体辨识,减少关节间耦合作用影响,避免多次识别实验环境不一致而产生的误差。最后采用最小二乘法计算机器人的动力学参数,解决因初始值选择不合理而导致辨识精度受限的问题。实验结果表明：此方法得到的最优激励轨迹能够满足约束条件,缩短优化时间,有效提高动态参数辨识的准确性和有效性。     

状态反馈条件下提高轧机伺服系统精度的算法研究

不确定性条件下的控制问题是现代控制中的一个重要课题。在大多数控制问题中,实际的控制对象和用于控制器设计的数学模型总是存在差异,这种差异来自于对象参数的不确定性和时变等。轧机液压位置伺服系统是具有参数不确定性、干扰因素多的复杂的非线性控制系统,所建模型较实际对象存在较大误差。为消除这种模型误差所产生的稳态误差,文中在稳态误差溯源及模型修正的基础上构建并联积分补偿、增益积分补偿、在线辨识自校正和动态反馈控制策略,对比仿真结果表明:动态反馈算法具有兼顾动静态性能指标的优势。     

基于最小二乘法和BP神经网络的磁流变阻尼器H-B模型参数辨识方法

针对Bingham模型磁流变阻尼器由于剪切稀化效应带来的阻尼力计算误差,在理论和仿真分析的基础上,提出一种最小二乘法和BP神经网络相结合的方法,对磁流变阻尼器H-B模型进行参数辨识,获得各参数与电流的关系,从而对磁流变阻尼器的阻尼力进行准确计算。最后通过磁流变阻尼器实验对理论方法进行验证。结果表明：借助于磁流变阻尼器的仿真分析,最小二乘法和BP神经网络相结合的磁流变阻尼器H-B模型参数辨识方法精确度高、吻合性好,验证了参数辨识结果的通用性及准确性。     

运动学误差模型变体下的机械臂标定精度研究

为进一步提高机械臂运动学标定精度,研究不同D-H建模方式下运动学误差辨识精度的差异,为运动学误差准确建模提供理论依据。围绕SD-H和MD-H运动学误差模型的各种变体形式,量化对比各模型变体的性态水平和线性化误差,并分析二者对运动学标定精度的综合影响。研究结果表明：x-z-y欧拉变换会破坏MD-H运动学误差模型最小性,不适用于运动学标定;其余满秩MD-H模型性态水平优于SD-H模型两个数量级;MD-H建模时引入的线性化误差与SD-H数量级等同;在模型性态水平和线性化误差综合作用下,较强测量噪声环境中的SD-H模型运动学标定精度(0.1%~39.3%)优于MD-H模型(0.1%~71.2%);角度类与位置类的运动学参数误差的辨识精度差异显著。研究成果为机械臂标定的精确建模以及误差类型与辨识方法的合理匹配提供了理论依据。     

A new method of virtual material hypothesis-based dynamic modeling on fixed joint interface in machine tools

The dynamic characteristics of joint interfaces affect the dynamic behaviors of a whole machine tool structure notably. An analytic method of virtual material hypothesis-based dynamic modeling on fixed joint interface in machine tools was conducted so as to improve the modeling accuracy of whole machine tools. The microcontact part of two contact surfaces in fixed joint interface was assumed as a virtual isotropic material, which is rigidly connected with two components composing fixed joint interface. The interaction between normal and tangential characteristics of fixed interface was taken into account, a set of analytic solutions of elastic modulus, shear modulus, Poisson ratio and density was deduced from virtual material by adopting Hertz contact theory and fractal theory. Using the finite element structural dynamic modeling approach in existence, when some parameters of material’s elastic modulus, Poisson ratio, density, etc. are known, the dynamic model for virtual material composing a component could be established; therefore, the dynamic model for whole structure, including joint interface, would be obtained. The theoretical mode shapes were compared with the experimental ones (qualitative comparison of similar mode shape and quantitative comparison of the corresponding natural frequency). The comparison results show that the theoretical mode shapes are in excellent agreement with the experimental ones. The relative errors between the theoretical natural frequencies and the experimental ones are less than 9%. The good agreements of theoretical results with experimental ones confirm analytic solutions for virtual material’s parameters. The present solutions would be useful to the precise dynamic modeling of fixed joint interfaces in CNC machine tools in practice.     

Characterizations and models for the thermal growth of a motorized high speed spindle

In this paper, the characterizing and modeling of the thermal growth of a motorized high speed spindle is reported. A motorized high speed spindle has more complicated dynamic, non-stationary and speed-dependent thermal characteristics than conventional spindles. The centrifugal force and thermal expansion occurring on the bearings and motor rotor change the thermal characteristics of the built-in motor, bearings and assembly joints. It was found that conventional static models using regression analysis and artificial neural network failed to give satisfactory model accuracy and robustness. An auto-regression dynamic thermal error model, that considers the temperature history and spindle-speed information, has been proposed and proved to improve the model accuracy. However, it was found that temperature-based thermal error models, that correlated thermal displacement of the rotating cutting tool to the temperature measurements on the spindle housing, were not robust. Many nonlinear and time-varying thermal sources, such as coolant jacket, motor air gap, motion joints and assembly interfaces influence thermal displacement. The relationship between temperature measurements and thermal displacements is highly nonlinear, time-varying and non-stationary. A new thermal model which correlates the spindle thermal growth to thermal displacements measured at some locations of the rotating spindle shaft has been proposed. It was found that the displacement-based thermal error model has much better accuracy and robustness than the temperature-based model.     

基于双激励信号的双惯量进给系统机械参数辨识

为了提高进给系统机械参数辨识的效率与精度,提出一种基于双激励信号的双惯量进给系统机械参数辨识方法,先采用伪随机二进制信号对机械传动环节进行频域辨识,根据系统的频响特性设计扫频信号频带,采用增广最小二乘法对双惯量动力学模型离散结构和扰动进行无偏估计,使用零极点匹配法将离散传递函数转化为连续时间传递函数,求解系统的机械参数。为了减小非线性摩擦的影响,整个实验过程均在加速中进行。最终模型的输出与实际输出拟合度达到98.61%,残差分析满足理论性能验证指标,提高了双惯量进给系统机械参数辨识精度与效率。     

六自由度工业机器人本体标定实验的研究

目前六自由度工业机器人普遍具重复定位精度较高,但绝对定位精度较低的特性,所以为了提高离线编程的精度,需要通过运动学建模、实际测量、参数辨识、误差补偿四步进行机器人本体标定。文中在概括总结现有的工业机器人本体标定技术的基础上,运用MATLAB进行运动学仿真建模,Leica AT960绝对激光跟踪仪系统进行实际测量,SA软件实现参数辨识,外部控制器进行关节补偿,完成本体标定实验。并在此基础上,按照GB/T12642-2013进行标定前后机器人位姿特性检测,通过Robot Check软件处理检测数据,对比前后结果,验证了本体标定实验。     

基于工具坐标标定改进的工业机器人零点位置自整定方法

在工业机器人应用过程中更换工具时需要重新标定工具坐标,一般情况下工具坐标通过固定点约束的3点法进行标定,此方式在机器人本身零点位置不准确的情况下往往会得到一个较差的结果。基于3点法中定点约束的原理,提出一种改进的工业机器人零点位置自整定方法,对出厂标定过的六自由度串联机器人,修改其零点位置与工具坐标的标准值产生给定误差,基于空间内一个固定点的约束关系,在已知机器人连杆参数的情况下建立机器人坐标变换模型,取21组机器人关节角与笛卡尔坐标数据作为辨识条件参数,机器人工具坐标与零点位置的偏差作为未知数据,通过最小二乘迭代算法计算出工具坐标与零点位置的偏差数据。将实验对象的整定结果和给定的误差参数进行对比,整定结果基本与给定误差一致,并通过比较自整定前后示教器上的位置显示值之间的误差值,证明了校准算法的可行性。     

基于分子动力学研究孔隙率对FeO/Fe界面裂纹扩展的影响

孔隙率是影响氧化皮与钢基体界面裂纹扩展的重要因素,采用分子动力学模拟软件LAMMPS建立模型Ⅰ(定孔缺陷数量模型)和模型Ⅱ(定孔缺陷尺寸模型)两种模型,从抗拉强度、中心对称分析(CSP)以及界面局部应力分布3个方面研究了孔隙率对于界面裂纹扩展的影响。结果表明,相同孔隙率下,模型Ⅱ比模型Ⅰ的抗拉强度低,表明孔缺陷分布范围比孔缺陷的尺寸对于材料的抗拉强度影响更大。同时裂纹扩展时动态不稳定性导致裂纹非对称性扩展,孔隙率的增加会阻碍应力增长和裂纹扩展速度,增强裂纹扩展的不对称性。当孔隙率达到9%时,在界面裂纹扩展后,模型Ⅰ+x方向裂尖偏转界面上方孔缺陷,模型Ⅱ则为界面裂纹扩展后,上方孔缺陷融合发展。     

基于自适应模糊神经网络焊接接头力学性能预测

通过对TC4钛合金进行TIG焊,并测定接头的抗拉强度、抗弯强度和断后伸长率,获得网络仿真所需的数据.结合使用BP算法与最小二乘相结合的混合算法,建立了用于焊接接头力学性能预测的自适应模糊神经网络模型.利用该模型进行仿真,其平均误差远小于7%.结果表明,该模型可根据焊接工艺参数对焊接接头的抗拉强度、抗弯强度和断后伸长率等力学性能进行较为准确的预测,并且具有建模快、模型简单、预测速度快、预测精度高,泛化能力强的优点,从而为焊接接头力学性能预测提供了一条有效的途径.     

质心测试平台的运动学及其误差模型研究

为了探讨质心测试平台的结构参数误差引起质心测试结果的偏差,在对质心测试平台进行运动学分析的基础上,依据矩阵全微分理论,建立了结构参数误差与运动平台姿态误差模型。最后,通过计算机仿真对误差模型进行了验证,结果表明,该误差模型能够反映实际姿态误差的变化。     

纯铜与不锈钢扩散焊接头性能及原子扩散动态解析

采用微观组织观察、硬度测试、拉伸试验、EPMA(电子探针)等手段研究了焊接工艺条件对纯铜与铁素体410L以及奥氏体304不锈钢扩散焊接接头性能的影响,结合理论计算对两种接头的原子扩散机理进行了解析.结果表明,在焊接压力、时间一定的条件下,接头的抗拉强度随焊接温度的升高而增大.Cu原子与41OL最适宜的焊接温度比Cu原子与304的大约小50 K,这是由于Cu原子向体心立方晶体(bcc)铁素体系中的扩散速度比在面心立方晶体(fcc)奥氏体系中快的缘故.Cu原子向不锈钢一侧扩散的实测值比理论值小是由于理论的初期条件是假定试样100%完全密接,而实际的接合界面却是粗糙不平,要达到接合初期的完全密接是需要时间的.     

Cartesian compliance model for industrial robots using virtual joints

Industrial robots represent a promising, cost-saving and flexible alternative for machining applications. Due to the kinematics of a vertical articulated robot the system behavior is quite different compared to a conventional machine tool. The robot’s stiffness is not only much smaller but also position dependent in a non-linear way. This article describes the modeling of the robot structure and the identification of its parameters with focus on the analysis of the system’s stiffness. Therefore a method for the calculation of the Cartesian stiffness based on the polar stiffness and the use of the Jacobian matrix is introduced. Furthermore, so called virtual joints are used. With this method it is possible to model each joint of the robot with three degrees of freedom. Beside the gear stiffness the method allows the consideration of the tilting rigidity of the bearing and the link deformations to improve the model accuracy. Based on the results of the parameter identification and the calculation of the Cartesian stiffness the experimental model validation is done.     

混合聚类RBF神经网络焊接接头力学性能预测

构建混合聚类算法,与伪逆法结合建立RBF神经网络模型预测焊接接头力学性能.以TC4钛合金TIG焊接试验为基础,将焊接参数作为模型输入,焊后接头力学性能作为模型输出.通过仿真,该模型预测平均相对误差范围为1.74%~6.69%,具有较高的预测精度、适应性和泛化能力,能够预测焊接接头力学性能.采用数学解析对所建模型分解,得到焊接工艺参数与接头力学性能之间映射关系的函数表达式,可优化焊接工艺参数.利用焊接专业知识对模型的径向基单元参数进行调整,提高了模型的预测精度,为将焊接专家知识融入RBF神经网络模型开辟了新方法与途径.     

基于数据驱动的数控机床自适应迭代学习控制

数控机床位置伺服系统受加工环境、零件形状和机床机电特性等变化因素的影响,其零件加工是一个典型的非线性、时变和不确定动力学变化过程,因此,建立其精确机制模型很困难。针对相同零件批量加工过程呈现的重复运行特点,基于被控对象的等价数据模型,提出一种基于数据驱动的自适应迭代学习控制方法。所提控制方法采用沿迭代轴的动态线性化方法,通过最小化控制目标函数,仅利用数控机床位置伺服系统的输入输出数据,实现学习控制增益的自适应更新,克服传统P型迭代学习控制方法固定增益的问题,并经过严格理论分析保证了该方法的收敛特性。仿真结果表明:提出的数据驱动自适应迭代学习控制方法,相比传统P型迭代学习控制方法,平均绝对误差和最大绝对误差分别减小了46%和56%。     

六辊冷轧机功效系数的研究

板形调控机构的功效系数是冷轧板形闭环反馈控制系统中最为关键的参数,直接影响到板形控制效果的好坏。针对有限元方法计算时间长和在线实验方法风险大、成本高等缺点,本文采用修正的影响函数法建立了六辊冷轧机的辊系弹性变形计算模型,通过求解轧后带钢横向平直度分布来确定各板形调控机构的功效系数,计算值与实测值的分布吻合得很好,轧辊弯辊功效系数的误差都小于1. 2%,轧辊倾斜功效系数的误差小于3. 5%。基于修正影响函数法建立的理论模型计算速度快,可以作为在线设定模型使用。     

SCARA机器人关节伺服系统中的干扰补偿控制

干扰是影响机电伺服系统性能的主要因素.SCARA机器人的第一和第二2个关节伺服系统具有复杂的动力学结构.综合考虑实际系统中模型参数不确定性和系统外部干扰,提出了采用干扰观测器(disturbance observer,DOB)进行干扰抑制的方法.该方法对负载变化、模型不确定性等干扰均能有效抑制,且实现简单,在抑制周期性负载交变干扰上,优于传统PID控制器.在HL863-R101型SCARA机器人关节伺服系统中进行了实验和验证,实验结果表明,DOB对SCARA机器人关节伺服系统的干扰有较强的观测和补偿控制能力.     

基于多体系统理论的数控机床加工精度几何误差预测模型

针对多轴联动数控机床加工精度误差补偿问题,从分析数控机床误差产生机制和建立精度误差补偿模型的角度,提出基于多体系统理论的数控机床加工精度几何误差预测模型。分析B-A摆头五轴龙门数控机床的拓扑结构关系、低序体阵列、各典型体坐标变换,推导出B-A摆头五轴龙门数控机床的精度几何误差预测函数模型。采用平动轴十二线法误差参数辨识算法,计算出B-A摆头五轴数控机床21项空间几何误差,为精度几何误差预测函数提供有效的误差参数。该精度误差参数建模方法,对不同结构和运动关系的数控机床具有通用性,为后续数控机床误差动态实时补偿提高切削加工精度提供了理论基础。     

A systematic optimization approach for the calibration of parallel kinematics machine tools by a laser tracker

Parallel kinematics machine has attracted attention as machine tools because of the outstanding features of high dynamics and high stiffness. Although various calibration methods for parallel kinematics machine have been studied, the influence of inaccurate motion of joints is rarely considered in these studies. This paper presents a high-accuracy and high-effective approach for calibration of parallel kinematics machine. In the approach, a differential error model, an optimized model and a statistical method are combined, and the errors of parallel kinematics machine due to inaccurate motion of joints can be reduced by this approach. Specifically, the workspace is symmetrically divided into four subspaces, and a measurement method is suggested by a laser tracker to require the actual pose of the platform in these subspaces. An optimized model is proposed to solve the kinematic parameters in symmetrical subspaces, and then arithmetical mean method is proposed to calculate the final kinematic parameter. In order to achieve the global optimum quickly and precisely, the initial value of the optimal parameter is directly solved based on the differential error model. The proposed approach has been realized on the developed 5-DOF hexapod machine tool, and the experiment result proves that the presented method is very effective and accurate for the calibration of the hexapod machine tool.