基于PSO-BP神经网络光电编码器误差补偿研究

考虑到高精度绝对式光电编码器应用广泛,其角度测量精度对整个系统精度影响较大,但由于角度传感器生产安装过程中产生的误差等原因,使得传感器在实际应用中存在一定的误差.而使用传统误差补偿方法难以得到较好的补偿效果,本文使用一种基于PSO的BP神经网络作为角度传感器误差补偿系统的算法.通过实验验证,该种算法能够对角度传感器误差进行较好的补偿,与补偿前相比,其标准偏差提高了12.5倍,最大误差和平均误差降低到9.6％和8.5％,提高了传感器检测精度.与使用了基于传统BP神经网络和基于多项式拟合算法的误差补偿系统进行对比实验,结果表明,其补偿效果亦优于这两种算法.     

一种光电编码器精度自动检测系统

随着光电编码器的广泛应用,市场对其需求也逐渐增大。利用自准直仪－金属多面棱体对光电编码器精度检测的传 统手动检测方法具有检测效率低的缺点,严重制约了光电编码器生产效率的提高。针对上述问题,设计了一种光电编码器精度自动检测系统,MATLAB程序控制电机带动待测光电编码器同步旋转,通过对比待测光电编码器及电机运动角度的数据计算待测光电编码器的精度,实现光电编码器精度自动检测。系统采用沿面检测算法减小测量结果的分辨率误差,采用傅里叶谐波分析方法对电机转角误差进行修正,进而提高了检测精度,使用该系统对某型号的 16位光电编码器进行检测.实验结果显示该系统检测精度不大于±２″,该系统具有结构简单,检测精度高的优点,可提高光电编码器的生产和检测效率。     

血液粘度仪旋转锥体角速度误差分析与补偿

剪切池内旋转锥体的角速度是新型锥板式血液粘度仪的主要信息源,其精度取决于光电传感器和齿形盘.分析了光电传感器的点光源特性和齿形盘的周期性摆动及其加工精度导致的角度误差.针对锥体角速度的单调递减特性、角度误差在单转内的随机性及整个测量过程中呈周期性,采用有理三次样条插值算法对角速度误差进行补偿.实验结果表明:该方法可提高...     

高精度光电编码器信号补偿技术的研究进展

近年来,由于多领域对于旋转控制需要的增长,光电编码器获得了广泛的应用,同时也对光电编码器的测角精度和分辨力提出了更高的要求。指出了光电信号补偿技术的发展为光电编码器精度的提高提供了重要保障;介绍了光电编码器的原理及衡量光电信号质量的4项指标;从此4项指标出发,总结了国内外的信号自适应补偿技术的研究现状;最后,分析了现有补偿方法的优点和存在的限制,展望了光电编码器信号补偿方法的发展趋势。     

基于 ＰＳＯ￣ＢＰ 神经网络的增量式拉线位移传感器误差补偿方法

拉线位移传感器将拉绳的位移转变为滑轮的转动,通过角度测量得到拉线自由端的位移,安装方式和使用环境都会对测量结果产生影响。本文基于拉线位移传感器的工作原理建立了拉线的力学模型,分析了误差影响因素,同时分析了其角度测量部件-增量式旋转编码器引入的误差。由于目前比较普遍的误差补偿模型优化算法难以获得较好的补偿效果,本文提出了一种基于粒子群算法（PSO）优化的BP神经网络模型的误差补偿方法。以KS120系列拉线位移传感器为研究对象,并进行了实验研究,结果显示：在全量程范围内,使用该方法进行误差补偿后的拉线位移传感器精度由0.136%FS提高到0.007%FS,提高了95%。最后将本模型与基于多项式拟合算法和传统BP神经网络算法的补偿系统进行实验对比,结果显示补偿效果亦优于这两种方法。     

测井缆长的误差建模及应用

为提高测井电缆缆长测量精度,给光纤陀螺连续测斜仪提供高精度缆长信息,对基于光电编码器的缆长测量方法进行了详细误差分析并建立了缆长拉伸误差数学模型。构建了基于FPGA@虚拟仪器的缆长测量系统并应用拉伸误差模型进行拉伸误差的软件补偿．实现了测井缆长的高精度测量。最后刺ABAQUS有限元软件对测量结果进行了仿真验证,结果表明。经误差补偿后缆长测量相对误差低于0．3‰,满足测斜仪对缆长精度的要求。     

改进的IMU传感器安装误差正交补偿方法

针对传统的正交补偿方法难以保证惯性测量单元具有较高的正交补偿精度的问题,提出了一种改进的适用于大角度和小角度安装误差角情形的正交补偿方法,该方法分别建立三轴加速度计和三轴光纤陀螺传感器的安装误差方程,对惯性测量单元进行速率标定和多位置静态标定,并利用最小二乘法求解惯性测量单元的安装误差方程参数.仿真和实验结果表明:该方法较传统的正交补偿方法具有较高的正交补偿精度和传感器标定精度,且回避了静态标定时在较大安装误差角下利用转位机构获得零偏存在较大误差的问题,大大地提高了标定效率.     

差频式悬浮轴振动测量传感器的设计

提出了一种新的悬浮轴振动测量传感器,它由导电介质电容传感器和反射式光电编码器构成组合传感器,振荡器和差频计数器构成信号处理电路,并利用角度等分采样和单片机控制实现振动位移测量功能。重点阐述了传感器各组成环节的设计思路与参数确定,给出了实验数据;介绍了利用反射式光电编码器实现等转角采样,还可以用于悬浮轴的转角、转速等其他参数的测量,实现了差频计数器被测频率信号与采样控制信号之间的同步锁定功能,消减了计数误差,提高了测量精度。     

基于PID的生物离心机自动定位控制研究

针对现有基于PID的生物离心机光电编码器定位精度误差大的问题,通过采用人工鱼群优化BP神经网络初始权值与阈值选择,提出一种生物离心机自动定位控制误差补偿方法。仿真结果表明,所提方法可有效补偿生物离心机光电编码器的误差,提高离心机自动定位精度,误差补偿后的最大误差为0.039°,最小误差为-0.013 5°,平均误差为4.86”,标准差为0.32,相较于误差补偿前,整体精度提高了3.34倍,具有一定的有效性。     

极端环境下光电编码器误差补偿方法

针对高、低温环境对光电编码器的影响和传感器的误差补偿方法进行研究。通过温度判别光电编码器所处环境分区,并切换不同的补偿方法实现经济高效的误差补偿。常温区采用直线最小二乘法补偿模型,高、低温区采用处理非线性拟合更优的最小二乘支持向量机(LS-SVM)补偿模型。通过实验装置测试可知:在高、低温区,光电编码器测量误差呈非线性,而在常温区光电编码器测量误差呈线性。研究的极端环境下光电编码器误差补偿方法无论对常温区域内还是对高、低温区呈非线性变化的测量误差均有很好的补偿作用。     

基于改进CORDIC算法的正余弦编码器信号细分技术

为了获取更精确的电机位置等实时反馈信息,提高数控系统等闭环控制系统的反馈精度,主要设计了基于改进坐标旋转数字计算(CORDIC)算法的正余弦编码器高分辨率信号处理方法.首先通过信号调理电路对编码器信号进行差分放大和整形滤波等处理;然后四倍频计数得到粗码信息,并通过基于改进CORDIC算法的电子学细分方法获得精插补位置信息;最后,将粗码和精码信息整合,得到高分辨率和高精度的电机角位置信息.实验结果显示,与算法优化改进前相比,该方法信号测量误差明显减少,精度较高.     

基于隧道磁阻效应的反正切转速测量方法

针对传统转速测试系统精度低、温度稳定性差、测量范围窄等问题,提出一种基于隧道磁电阻(TMR)效应的高性能转速测试系统.在没有误差校正的情况下实现了0.19°的位置检测精度,已经达到了工程应用中常见的2000线编码器精度.本方法在被测转轴上布置磁栅环,利用具有高分辨率高频响特性的隧道磁电阻传感器对磁栅微弱磁场变化进行检测,得出频率随转速变化的正余弦信号.实验分别采用定时测角法、定角测时法和反正切法计算转速,结果表明3种算法中反正切算法检测精度最高,可达1.25％,更适合于隧道磁阻转速测量系统.     

Validation of smartphone gyroscopes for mobile biofeedback applications

Smartphones are currently the most pervasive wearable devices. One particular use of smartphone inertial sensors is motion tracking in various mobile systems and applications. The objective of this study is to validate smartphone gyroscopes for angular tracking in mobile biofeedback applications. The validation method includes measurements of angular motion performed concurrently by a smartphone gyroscope and a professional optical tracking system serving as the reference. The comparison of the measurement results shows that the inaccuracies of a calibrated smartphone gyroscope for various movements are between 0.42° and 1.15°. Based on the measurement results and the general requirements of biofeedback applications, smartphone gyroscopes are sufficiently accurate for angular motion tracking in mobile biofeedback applications.     

An algorithm for high precision attitude determination when using low precision sensors

The technology for high precision attitude determination when using low precision sensors is a key requirement of a modern small satellite. This paper presents a new attitude determination algorithm termed preprocess EKF(PP-EKF) based on preprocessing of the sensor data. It can enhance the overall modeling accuracy by using the quadratic penalty function to correct the dynamic model error and angular velocity error in realtime, based on the information fusion of the current and the past measurement information. The measurement model of the EKF is linearized by introducing the q method. The solution error of this process is also corrected to further improve the accuracy of the measurement model and make better use of measurement data from the low precision sensors, to obtain good attitude determination results. Finally, the simulation results demonstrate the high reliability and other advantages of the proposed algorithm.     

Optimization of Intelligent Approach for Low-Cost INS/GPS Navigation System

Due to the inherent highly nonlinear vehicle state error dynamics obtained from low-cost inertial navigation system (INS) and Global Positioning System (GPS) along with the unknown statistical properties of these sensors, the optimality/accuracy of the classical Kalman filter for sensor fusion is not guaranteed. Therefore, in this paper, low-cost INS/GPS measurement integration is optimized based on different artificial intelligence (AI) techniques: Neural Networks (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) architectures. The proposed approaches are aimed at achieving high-accuracy vehicle state estimates. The architectures utilize overlapping windows for delayed input signals. Both the NN approaches and the ANFIS approaches are used once with overlapping position windows as the input and once with overlapping position and velocity windows as the input. Experimental tests are conducted to evaluate the performance of the proposed AI approaches. The achieved accuracy is presented and discussed. The study finds that using ANFIS, with both position and velocity as input, provides the best estimates of position and velocity in the navigation system. Therefore, the dynamic input delayed ANFIS approach is further analyzed at the end of the paper. The effect of the input window size on the accuracy of state estimation is also discussed.     

A new method for digital encoder adaptive velocity/acceleration evaluation using a TDC with picosecond accuracy

In this paper, a new methodology for deriving the velocity and the acceleration information of a digital encoder through processing its pulse train, is presented. The proposed method is based on accurate time measurement (with picosecond accuracy) as well as encoder pulse counting in adaptively changing time intervals, providing thus a wide-range velocity evaluation with very good accuracy. The method offers better response times at low speeds and very high-accuracy at the full range of measured velocities. By using the proposed method, the velocity measurement accuracy is improved compared to currently known methods, since high-resolution time-to-digital converters (TDC) are included in the design. The increased accuracy in velocity measurement allows the application of the simple arithmetic differentiation method on the velocity information in order to derive the acceleration, which in other cases would not be suggested due to accumulated quantization noise. A digital signal processor (DSP) also allows the implementation of numerous other methods to calculate acceleration. The proposed configuration has been implemented in specific hardware (FPGA), reserving thus the computational power of the system controlling DSP for high-level control tasks.     

Humanoid odometric localization integrating kinematic,inertial and visual information

We present a method for odometric localization of humanoid robots using standard sensing equipment, i.e., a monocular camera, an inertial measurement unit (IMU), joint encoders and foot pressure sensors. Data from all these sources are integrated using the prediction-correction paradigm of the Extended Kalman Filter. Position and orientation of the torso, defined as the representative body of the robot, are predicted through kinematic computations based on joint encoder readings; an asynchronous mechanism triggered by the pressure sensors is used to update the placement of the support foot. The correction step of the filter uses as measurements the torso orientation, provided by the IMU, and the head pose, reconstructed by a VSLAM algorithm. The proposed method is validated on the humanoid NAO through two sets of experiments: open-loop motions aimed at assessing the accuracy of localization with respect to a ground truth, and closed-loop motions where the humanoid pose estimates are used in real-time as feedback signals for trajectory control.     

Navigation of non-holonomic mobile robot using neuro-fuzzy logic with integrated safe boundary algorithm

In the present work, autonomous mobile robot (AMR) system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neuro-fuzzy inference system (ANFIS) controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.     

基于Arduino的磁编码器轴角解算系统设计

磁编码器轴角解算技术是伺服系统中反馈电机运动状态的关键技术之一.磁编码器轴角解算的精度是决定伺服系统精度的关键因素之一.为提高轴角解算的精度,基于霍尔式磁编码器工作原理,设计了基于Arduino的磁编码器轴角解算系统.该系统以Arduino作为主控制器,由正交安装在磁铁下方的2个霍尔元件构成磁编码器.磁编码器感应磁场变...     

基于IQPSO-GA优化ANFIS模型的服务器故障预警方法

针对服务器底层部分业务类硬件故障对系统稳定运行的影响,提出一种改进的量子行为粒子群优化(IQPSO)与遗传算法(GA)相结合的混合元启发式优化算法对自适应神经模糊推理系统(ANFIS)参数进行训练,以获得更准确的ANFIS规则进行硬件故障预警的方法。首先,通过分析服务器业务与硬件相关参数之间的映射关系,通过采集的数据集对ANFIS模型进行训练构造预测模型;其次,考虑ANFIS在梯度计算过程中存在容易陷入局部最优值的问题,设计了一种IQPSO算法结合GA中的交叉和变异算子操作混合元启发算法全局搜索ANFIS规则参数;最后,通过一组后处理样本数据集对所提方法有效性和稳定性进行了检验。实验结果表明,该方法可有效预警服务器硬件故障,基于所提混合元启发优化算法获得的ANFIS模型具备更快的收敛速度和更高的全局搜索精度,与传统ANFIS模型相比泛化精度提高了47%以上。