具有圆度误差的径向金属密封接触应力研究

由于加工圆度误差的影响,井下流量控制阀径向金属密封接触应力分布不均匀,从而影响密封性能。利用有限元方法研究具有圆度误差的径向金属密封唇部接触应力分布,并分析圆度误差对径向金属密封接触应力的影响;基于有限元分析结果提出径向金属密封接触应力分布的理论解析式,并进行误差分析。具有圆度误差的径向金属密封唇部接触应力分布的理论解与数值解相符,各参数引起的最大接触应力的平均相对误差约为10%。根据具有圆度误差的径向金属密封副接触应力的分布规律,提出合理的过盈量函数,修正了径向金属密封轴对称结构的悬臂梁模型的接触应力理论关系式,得出了圆度误差下的径向金属密封接触应力分布规律。研究结果为井下流量控制阀径向金属密封的设计提供了理论指导。

不确定电液伺服系统的时变输出约束自适应滤波控制

针对电液伺服系统位置跟踪控制中存在的输出约束和不确定性问题,提出一种基于正切型时变障碍Lyapunov函数的输出约束自适应滤波控制方法。构造具有时变约束边界的正切型时变障碍Lyapunov函数,通过时变边界函数的参数设置,使系统输出具有较好的瞬态和稳态性能;设计径向基函数(RBF)神经网络及权重自适应学习律,在线逼近由模型不确定性和未知干扰组成的复合干扰,并将逼近值用于反馈控制;采用二阶指令滤波反步法设计状态反馈控制律和误差补偿机制,避免反步设计中“计算爆炸”的问题,同时消除滤波误差,提高系统位置跟踪精度;依据Lyapunov稳定性理论证明闭环系统中所有误差信号的收敛性。仿真结果表明:系统的稳态误差在所提方法下约为3.48×10^(-8)m,相比于其他控制方法,跟踪误差始终约束在时变的约束边界内,跟踪精度和控制性能均得到提升。

基于NSGA-Ⅱ传感位置优化的曲面重构及误差补偿方法

通过优化光纤布拉格光栅形状传感技术中传感点位置和补偿重构结果来提高薄层合金板三维形状重构精度。通过ANSYS workbench建立合金板仿真模型,提取应变和位移模态振型,根据模态置信准则、转换矩阵稳定性和模态振型相似性分别设计了三个目标函数,采用快速和精英机制的多目标遗传算法优化传感器位置。将镍钛合金板弯曲成不同曲率半径的弧形,利用光纤布拉格光栅中心波长漂移量和线性插值算法计算得出不同形状下的结构应变,重构合金板形状,均方根误差和最大误差相较于单目标优化算法分别减小30%和15%。利用粒子群优化径向基函数神经网络算法拟合误差与位移的关系实现误差补偿,均方根误差和最大误差比无补偿时分别减小了90%和70%,最大相对百分比误差仅为5%,提高了三维形状重构算法精度。

基于微形变分析的电容式MEMS加速度计温漂误差精密估计方法

针对传统的电容式微机电系统(micro-electro-mechanical system,MEMS)加速度计(capacitive MEMS accelerometers,CMA)温漂误差(temperature drift error,TDE)补偿方法存在非精准溯源TDE致使TDE估计精度低、反复尝试估计模型构型导致构建过程复杂繁琐的问题,提出基于微形变分析的CMA TDE精密补偿方法。首先,通过微形变分析内部结构精准溯源TDE,基于径向基函数神经网络(radial basis function neural network,RBFNN)构建改进型TDE精密估计模型;其次,基于专家经验和模糊理论提出Expert-Fuzzy辅助决策下TDE估计模型辨识方法,为改进模型提供有效的构型指导;然后,设计升温试验测试CMA,构建传统模型和改进模型并通过对比其输出偏置稳定性评估TDE估计性能。实验结果表明,改进模型构建过程大大简化,补偿后CMA偏置稳定性提升约35%。本方法能够更加精准地估计TDE,有效解耦硅基材料的温度依赖性并提升CMA的环境适应性。

混合神经网络用于滚珠丝杠热误差预测

为了减小数控机床热致定位误差影响,提高机床加工精度,这里建立了径向基神经网络(RBFNN)和时间序列(ARIMA)混合模型的变权值热误差预测方法。综合两个单一模型对数控机床热误差进行预测,利用逆向辨识优化算法分别获取两个单一模型的优化权值,得到变权值混合模型,使得热误差预测精度得到提高。将这里混合模型与RBFNN模型和ARIMA模型分别进行对比分析,结果表明混合模型(RBFNN-ARIMA)的预测精度明显优于单一RBFNN和单一ARIMA模型,证明了此算法的有效性。

异形外圈圆柱滚子轴承径向游隙测量装置设计

针对异形外圈圆柱滚子轴承在高精度、定量载荷条件下的径向游隙测量问题,基于静、动态相结合的测量原理完成了异形外圈圆柱滚子轴承径向游隙测量装置设计。介绍了整机结构以及具体操作步骤,并根据异形外圈圆柱滚子轴承特殊的外圈结构以及自重大引起摩擦力较大的问题采取了加载、测量位置可调整机构以及导轨替代滑动摩擦的设计;对形变误差、径向跳动误差和其他误差进行了分析,各部分误差相对较小。

考虑中心距变化的齿轮接触应力计算方法

齿轮制造/安装误差和轴承径向间隙导致齿轮中心距变化,改变齿面的接触特征,影响齿面的磨损和疲劳寿命。本文基于考虑制造/安装误差和轴承径向间隙影响的2种齿轮中心距模型,结合赫兹接触理论和齿轮啮合原理,建立了考虑中心距变化的齿面接触应力计算模型,并通过将该模型所得结果与有限元接触模型结果对比,验证所提出模型的准确性。此外,进一步研究了齿轮中心距变化对齿面接触应力的影响。研究结果表明:随着实际中心距的增大,本文模型结果与有限元模型结果的误差减小。随着齿轮中心距的增大,单齿对啮合区沿啮合线方向左移,齿面接触应力减小,但齿轮重合度也减小。因此,合理控制齿轮中心距变化对齿面接触性能的提升具有重要意义。

圆柱滚子轴承摩擦力矩试验机

针对现阶段圆柱滚子轴承摩擦力矩试验机测量载荷较小以及大径向载荷条件下测量精度不高的问题,基于平衡法研制了圆柱滚子轴承摩擦力矩试验机,试验机主体为卧式结构,最大载荷可施加至10 k N,可以在不同转速和润滑条件下测量圆柱滚子轴承的摩擦力矩。基于试验分析了径向载荷对圆柱滚子轴承摩擦力矩的影响规律,结果表明圆柱滚子轴承摩擦力矩随径向载荷的增加逐渐增大,摩擦力矩测量值与理论计算值相差较小,验证了圆柱滚子轴承摩擦力矩试验机的实用性。

一种WOA-RBF的BDS精密卫星钟差短期预报方法

针对当前北斗卫星导航系统(BDS)精密卫星钟差预测模型精度不高、预测误差较大等问题,提出一种将鲸鱼优化算法(WOA)和径向基函数神经网络(RBF)相结合的钟差预测模型—鲸鱼算法优化的RBF组合模型(WOA-RBF):利用四分位法和分段线性插值法完成数据预处理,通过鲸鱼优化算法对RBF中的扩展速度和输出层线性权重进行寻优,得到最优参数,最终得到优化后的输出结果。实验结果表明:与二次多项式(QP)模型、灰色模型(GM)和径向基函数神经网络(RBF)模型相比,WOA-RBF模型优势明显。在预报时长为6 h时,均方根误差在0.25 ns以内;在预报时长为12 h时,均方根误差在0.27 ns以内,证明了WOA-RBF模型在精密卫星钟差短期预报中的准确性和可行性。

An Error Equivalent Model of Revolute Joints with Clearances for Antenna Pointing Mechanisms

Joint clearances in antenna pointing mechanisms lead to uncertainty in function deviation. Current studies mainly focus on radial clearance of revolute joints, while axial clearance has rarely been taken into consideration. In fact, own?ing to errors from machining and assembly, thermal deformation and so forth, practically, axial clearance is inevitable in the joint. In this study, an error equivalent model(EEM) of revolute joints is proposed with considering both radial and axial clearances. Compared to the planar model of revolute joints only considering radial clearance, the journal motion inside the bearing is more abundant and matches the reality better in the EEM. The model is also extended for analyzing the error distribution of a spatial dual?axis("X–Y" type) antenna pointing mechanism of Spot?beam antennas which especially demand a high pointing accuracy. Three case studies are performed which illustrates the internal relation between radial clearance and axial clearance. It is found that when the axial clearance is big enough, the physical journal can freely realize both translational motion and rotational motion. While if the axial clearance is limited, the motion of the physical journal will be restricted. Analysis results indicate that the consideration of both radial and axial clearances in the revolute joint describes the journal motion inside the bearing more precise. To further validate the proposed model, a model of the EEM is designed and fabricated. Some suggestions on the design of revolute joints are also provided.

Synchronization of chaos using radial basis functions neural networks

The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.

THE CRUCIAL CONSTANTS IN THE EXPONENTIAL-TYPE ERROR ESTIMATES FOR GAUSSIAN INTERPOLATION

It＇s well-known that there is a very powerful error bound for Gaussians put forward by Madych and Nelson in 1992. It＇s of the form｜f（x） - s（x）｜≤（Cd）c/d｜｜f｜｜h where C, c are constants, h is the Gaussian function, s is the interpolating function, and d is called fill distance which, roughly speaking, measures the spacing of the points at which interpolation occurs. This error bound gets small very fast as d → 0. The constants C and c are very sensitive. A slight change of them will result in a huge change of the error bound. The number c can be calculated as shown in [9]. However, C cannot be calculated, or even approximated. This is a famous question in the theory of radial basis functions. The purpose of this paper is to answer this question.

HUID:DBN-Based Fingerprint Localization and Tracking System with Hybrid UWB and IMU

High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.

抗关节角度突变的多机械臂运动轨迹优化方法

机械臂运动轨迹易受到机械臂振动幅度、关节冲击力和障碍物的影响,导致运动轨迹过长,且遇到关节角度突变问题时,影响轨迹运动效果,为了解决上述问题,提出抗关节角度突变下多机械臂运动轨迹优化方法。建立以减少多机械臂运动幅度和关节运动冲击为目标的运动学模型,运用该模型对多机械臂关节之间的位置误差进行补偿,以提高多机械臂的运动精度。将多机械臂样本输入到RBF神经网络模型中得到平滑的运动曲线,输出最佳优化轨迹,完成多机械臂运动轨迹优化。实验结果表明,所提方法的关节转角控制效果较好,鲁棒性较强,有效缩短了运动轨迹长度,能够有效抑制关节角度突变问题。

B-Spline-Based Curve Fitting to Cam Pitch Curve Using Reinforcement Learning

Directly applying the B-spline interpolation function to process plate cams in a computer numerical control(CNC)system may produce verbose tool-path codes and unsmooth trajectories.This paper is devoted to addressing the problem of B-splinefitting for cam pitch curves.Considering that the B-spline curve needs to meet the motion law of the follower to approximate the pitch curve,we use the radial error to quantify the effects of thefitting B-spline curve and the pitch curve.The problem thus boils down to solving a difficult global optimization problem tofind the numbers and positions of the control points or data points of the B-spline curve such that the cumulative radial error between thefitting curve and the original curve is minimized,and this problem is attempted in this paper with a double deep Q-network(DDQN)reinforcement learning(RL)algorithm with data points traceability.Specifically,the RL envir-onment,actions set and current states set are designed to facilitate the search of the data points,along with the design of the reward function and the initialization of the neural network.The experimental results show that when the angle division value of the actions set isfixed,the proposed algorithm can maximize the number of data points of the B-spline curve,and accurately place these data points to the right positions,with the minimum average of radial errors.Our work establishes the theoretical foundation for studying splinefitting using the RL method.

采用双RBF神经网络控制的机械手末端位姿研究

为了提高并联机械手末端位姿定位精度和数控机床加工精度,采用双径向基函数(RBF)神经网络控制器,并对机械手定位误差进行仿真验证。给出平面并联机械手运动装置简图,分析并联机械手末端运动位置,推导出执行器运动速度和加速度方程式。设计并联机械手双RBF神经网络控制器,采用Matlab软件对并联机械手运动位姿进行仿真,并与单RBF神经网络控制器进行对比。结果显示:在单RBF神经网络控制器作用下,并联机械手横向位移、纵向位移和平台角位移跟踪误差较大;在双RBF神经网络控制器作用下,并联机械手横向位移、纵向位移和平台角位移跟踪误差较小。采用双RBF神经网络控制器,可以提高并联机械手末端位姿定位精度,从而提高数控机床的加工精度。

基于径向基网络的工业机器人误差补偿方法

工业机器人的定位精度是衡量机器人性能的一个重要指标,但由于工业机器人实际运动学参数值与名义值之间存在差异,在实际应用中不可避免产生定位误差。虽然工业机器人通常具有较高的重复定位精度,但是绝对定位精度一般无法满足高精度制造业的要求。因此,提升工业机器人的绝对定位精度具有一定的工程意义。为了提高工业机器人的绝对定位精度,提出一种基于径向基神经网络的工业机器人定位误差补偿方法,建立了工业机器人末端执行器的名义位置和实际误差之间的映射关系,同时使用粒子群优化算法优化神经网络形参数以提升网络模型精度。利用神经网络在指定工作空间中构建工业机器人定位误差场,通过预偏置指令位置的方式进行工业机器人定位误差补偿。仿真结果表明,所提方法能有效地提高工业机器人的绝对定位精度。

轿车子午线轮胎侧向松弛长度的测试及表征

以轿车子午线轮胎为研究对象,对轮胎侧向松弛长度的测试及表征进行研究。结果表明:轮胎侧偏角阶跃测试方法属于一阶线性非时变系统,仿照一阶RC电路的阶跃响应,设定不同的时间常数数据检查点,可以提高数据拟合优度;提出一种基于先加载后转角的转动角阶跃测试方法,可以此进行测试数据的时间修正;当试验台对侧偏角的输入与反馈调校后并一次性按测试序列完成所有测试时,测试系统的时间延迟误差基本稳定,考虑时间延迟与未考虑时间延迟计算得到的轮胎侧向松弛长度差异随轮胎速度的增大呈正比放大趋势;在高负荷工况下,轮胎侧向松弛长度在小侧偏角区间发生交叠。研究结果可提高用于评估或验证单胎侧向动力学瞬态特性的数据分析效率和准确度。

径向基函数在高超声速热流插值中支撑点选择的判据研究

在使用径向基函数进行高超声速热流插值时,为了避免传统单一绝对误差判据选择得到的支撑点在热流较小处插值不准确的问题,提出一种双重误差判据下的径向基函数插值过程。该过程首先使用绝对误差判据标准选择一定数量的支撑点,再使用相对误差判据标准选择另一部分点。通过数值实验验证同时采用以绝对误差和相对误差为标准选取支撑点的方法,结果表明:采用双重误差判据可以同时保证热流较大与较小处的插值精度,尤其能避免传统单一绝对误差判据导致插值结果中容易出现负热流的问题。

Optimizing circumferential assembly angle of rotor parts connected by curvic couplings based on acquired tooth surface error data

Due to the excellent self-centering and load-carrying capability,curvic couplings have been widely used in advanced aero-engine rotors.However,curvic tooth surface errors lead to poor assembly precision.Traditional physical-master-gauge-based indirect tooth surface error measurement and circumferential assembly angle optimization methods have the disadvantages of high cost and weak generality.The unknown tooth surface fitting mechanism is a big barrier to assembly precision prediction and improvement.Therefore,this work puts forward a data-driven assembly simulation and optimization approach for aero-engine rotors connected by curvic couplings.The origin of curvic tooth surface error is deeply investigated.Using 5-axis sweep scan method,a large amount of high-precision curvic tooth surface data are acquired efficiently.Based on geometric models of parts,the fitting mechanism of curvic couplings is uncovered for assembly precision simulation and prediction.A circumferential assembly angle optimization model is developed to decrease axial and radial assembly runouts.Experimental results show that the assembly precision can be predicted accurately and improved dramatically.By uncovering the essential principle of the assembly precision formation and proposing circumferential assembly angle optimization model,this work is meaningful for assembly quality,efficiency and economy improvement of multistage aero-engine rotors connected by curvic couplings.