传感器动态非线性的一种补偿方法

针对传感器存在的动态非线性问题，本文提出一种补偿方法，将具有动态非线性的传感器分为非线性静态环境环节和线性动态环节。先对传感器输出信号进行非线性静态校正，再进行线性动态补偿，研制以DSP为处理核心的传感器模拟器和传感器动态非线性补偿系统，实验结果表明，方法是有效的。     

基于最小二乘支持向量机的传感器非线性动态补偿

提出了一种基于最小二乘支持向量机的非线性传感器动态测量误差的校正方法，使得通过该方法补偿的传感器具有理想的输入输出特性。先将传感器的非线性动态系统分解成线性动态子环节和非线性静态子环节串联；与之对应，非线性动态补偿过程也包含2个阶段：线性动态补偿和非线性静态校正。然后，通过函数展开将补偿器的非线性传递函数转换为等价的类线性形式一中间模型；再通过LS-SVM回归算法求取中间模型参数；最后，推导出中间模型参数与补偿器2个子模型参数之间的关系，并通过该关系实现非线性静态校正和线性动态补偿环节的同时辨识。与常规非线性动态补偿方法比较，该方法优点是明显的：（1）只需进行一次动态标定实验；（2）能给出非线性动态补偿器的数学解析表达式；（3）充分利用LS—SVM的优点，使辨识的补偿器具有更好的抗干扰能力。仿真与实际实验结果均表明该传感器非线性动态补偿方法有效。     

传感器动态性能的研究进展

传感器的动态特性直接影响测试系统功能的发挥,国内外对传感器动态性能进行了大量的研究,回顾了传感器辨识建模和动态补偿等的研究进展与成果,并分析了传感器动态特性的研究前景.     

力传感器模型辨识及动态补偿器设计北大核心

力传感器作为测控系统的最前端,其动态特性对测控系统特性有本质影响。为建立有效改善力传感器动态特性的补偿器方法,根据非线性最小二乘法建立了力传感器的动态数学模型,基于零极点配置法设计了力传感器的动态补偿环节。针对以上方法进行了系统辨识实验与动态补偿环节仿真设计。实验与仿真结果表明,非线性最小二乘法能够切合实际地建立非线性系统的辨识模型,零极点配置法所设计的动态补偿环节极大地改善了传感器的动态特性,可以将该补偿方法应用于工程实际当中。     

压力传感器动态性能分析与动态补偿

在对炮口冲击波测试中，压力传感器的动态性能指标是否满足测量要求至关重要。本文通过GLS(SF)方法建立压力传感器的数学模型，并由数学模型求出动态性能指标。然而该传感器的动态性能指标不能满足测量要求，针对此问题本文采用零极点相消的方法设计出动态补偿滤波器，明显提高了该传感器的动态性能，最终解决了该冲击波的测量问题。     

杆式风洞应变天平动态解耦-补偿

采用串行动态解耦-补偿网络实现多维力/力矩传感器的动态解耦-补偿,对已有的设计方法进行了改进。基于单通道加载实验数据,通过系统辨识方法设计网络的各个环节。具体针对六维杆式风洞应变天平,采用负阶跃单元加载法进行动态标定实验。对力矩加载实验数据进行预处理。采用OE模型描述串行动态解耦-补偿网络中的各个环节,通过基于预报误差的系统辨识方法确定其参数。对实验数据处理的结果表明,所设计的杆式风洞应变天平动态解耦-补偿网络能将杆式天平的维间动态耦合误差由高至88.85%降低至6%以内,主通道阶跃响应调节时间缩短至30 ms以内且超调量降低至5%以下,从而大幅度改善了杆式风洞应变天平的动态性能。     

称重传感器自适应动态补偿方法

本文根据称重传感器的动态特性随着被测量而变化这一特点，提出自适应动态补偿方法，设计的动态补偿器能跟踪传感器模型的变化，始终保证测量系统动态响应的快速性。     

力传感器动态特性测试与性能改进

提出了一种力传感器的动态校准，介绍了用有源模拟滤波器改善力传感器动态性能的方法，包括动态补偿模拟滤波器的设计方法，力传感器动态器及其电路，动态补偿模拟滤波器电路，动态性能改善（时域和频域）的实验结果。     

基于虚拟仪器技术的某型飞机电传模拟器传感系统动态特性补偿仪

介绍了一种基本虚拟仪器技术的电传模拟器传感系统动态特性补偿仪，重点讨论了该补偿仪的虚拟仪器配置方案，数字滤波原理及其软、硬件实现方法，并且根据系统参数对补偿效果的影响，设计了一种自动查找最优补偿算法。     

腕力传感器三维自适应动态补偿方法

在已有的工作基础上，采用机理分析法和实验建模法，确定传感器极点随负载变化的规律，提出三维自适应动态补偿方法，解决动态补偿器自动跟踪传感器模型变化以及多维动态补偿问题。     

基于遗传神经网络的机器人腕力传感器动态建模与补偿方法

介绍用于MotomamV3X机器人上的新型多维腕力传感器,比较遗传算法与人工神经网络的特点,将遗传算法的交叉和变异操作进行改进,提出一种融合改进遗传算法(Genetic algorithm, GA)的函数连接型人工神经网络(Functional link artificial neural network, FLANN),并将其用于所介绍的新型机器人腕力传感器动态建模与动态性能补偿中。介绍动态建模与动态补偿原理及改进遗传神经网络算法,给出该传感器的动态模型和动态补偿模型。该方法利用腕力传感器的动态标定数据,采用改进遗传神经网络搜索和优化模型参数,保留了遗传算法的全局搜索能力和FLANN结构简单,鲁棒性好,且具备自学习能力的特点,克服了FLANN容易陷入局部极小的缺陷,具有快的网络训练速度及高的动态建模精度。理论分析和试验结果都证实了所提出的动态建模与动态补偿方法的有效性。     

无轴承永磁同步电机的神经网络逆控制

无轴承永磁同步电机是一个强耦合的非线性复杂系统,实现无轴承永磁同步电机的线性化解耦控制,是无轴承永磁同步电机稳定运行和走向实用化的关键。将神经网络具有的特点（对非线性系统的逼近能力以及对系统参数变化的适应能力）与逆系统方法的特点（解耦线性化）相结合,提出了基于神经网络的无轴承永磁同步电机逆系统解耦控制方法。通过用静态神经网络加积分器来构造无轴承永磁同步电机的逆系统,将无轴承永磁同步电机动态解耦成位移子系统和转速子系统分别设计调节器进行控制,然后运用线性系统理论进行综合。仿真及实验结果表明,系统具有良好的鲁棒性和动静态解耦性能。

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基于非线性GA算法的动态P模型的参数辨识与验证

针对现有的GMM-FBG电流传感器的磁滞非线性问题,提出了一种改进的动态Preisach磁滞模型。采用非线性遗传算法对改进动态Preisach磁滞数学模型进行参数辨识,提高了动态磁滞曲线的预测精度。运用改进动态Preisach模型对GMMFBG电流传感器进行建模及实验验证,实验及仿真结果表明该模型具有较好的预测性,预测误差在3.0%以内。经过磁滞补偿使得传感系统电流的测量灵敏度达到0.050 nm/A。     

Time domain modeling of cup anemometers using artificial neural networks

Under rapidly fluctuating wind speed, high inertia cup anemometers have a tendency to overspeed. The main objective of this article is to develop an inverse time domain model that could be used in real-time during cup anemometer operation to minimize the so-called “u-error”. A model proposed by Kristensen and an artificial neural network (ANN) direct model were first investigated to simulate the dynamic behavior of a heated cup anemometer with relatively high rotor inertia. Once the anemometer behavior was known, several virtual inputs were generated and the direct model was used to predict the instrument behavior. These models were built to emphasis the non-linear relationship between the free stream fluctuating wind and the wind speed measured by the anemometer. A semi-empirical inverse model derived from Kristensen's model was then studied and an ANN inverse model was suggested in order to minimize the so-called u-error. A methodology is proposed to gather the appropriate data to create both the direct and inverse model using an artificial neural network. The output of each model was compared with experimental data for validation and good agreement was found between the ANN models and the experimental data used for validation.     

基于动态阈值神经网络模型的Flow Shop排序研究

根据神经元的动作特征，提出了一种基于动态阈值的神经网络模型，用于求解Flow Shop排序问题，研究表明，这种模型能简化网络运行的中间过程，修正二值输出函数的性能，模型复杂性的降低使收敛速度和有效性得到了较好的改善，模型具有的模拟退火效果使系统跳出局部最优而收敛于全局最优的可能性增大。     

冲击波压力传感器动态特性补偿及其应用

为修正压力传感器动态特性引起的测试误差,避免传感器动态建模误差影响补偿结果,提出了一种基于量子粒子群优化(QPSO)算法和均方误差的传感器动态补偿方法。通过对传感器进行逆建模,寻优得到了最优阶次的补偿器系数,利用激波管动态校准实验对该方法进行了验证,分析了补偿前后传感器的时域与频域特性。结果表明,该方法有效扩展了传感器的工作频带;在实弹测试中,减小了动态测量误差,提高了测试精度。     

基于神经网络的复杂曲面加工误差控制

在复杂曲面的切削过程中，加工系统表现显著的多输入多输出及非线性特征，传统的误差补偿方法不能有效地保证加工精度，因此提出一种加工误差控制方法，引入神经网络对加工系统的逆模型进行辨识，运用该模型前置校正加工系统以改善加工效果，充分考虑到机械系统的非线性特征，且网络模型可连续辨识，因而系统的静态性能和动态特性均能有效补偿，在中凸变椭圆活塞裙面加工中的成功应用，证明其合理性及先进性。     

Dynamic characteristics and optimization research on PVDF piezoelectric film force sensor for steel ball cold heading machine

According to cold heading process with overloaded craft, high-impact dynamic real-time measurement requirements, this paper presents researches on dynamic characteristics and optimization of PVDF piezoelectric film force sensor for steel ball cold heading forming quality monitoring, through the combination method of mechanism analysis, mathematical modeling, numerical simulation and experimental validation. The motivation and strategic objectives are to breakthrough dynamic time-varying impacting load measuring fundamental technologies in steel ball forging process. The structure of piezoelectric film force sensor is proposed. The theoretical calculation formula of natural frequency is deduced and calculated by using MATLAB software. The mechanical performance analysis on dynamic model and structural optimization simulation by FEM is carried out. In order to study the validity of the proposed method, a prototype of the sensor is fabricated. The static and dynamic calibration devices are designed to realize calibration experiments on the fabricated PVDF piezoelectric film force sensor. The differences among experimental value, simulation value and the theoretical value are given. The nonlinear error of the fabricated sensor is 0.197%. The sensor’s first order natural frequency value is 5238 Hz. It is proved that the PVDF piezoelectric film force sensor has superior dynamic performance and high accuracy for measuring deformation in steel ball. The paper will provide important scientific basis and technical foundation to achieve superior performance steel ball.     

基于神经网络的气体传感器故障诊断

该文介绍了一种基于人工神经网络进行了气体传感器故障检测的新方法，文中利用单个气体传感器的输出信息为气体传感器建立了动态非线性神经网络气体传感器输出模型，并利用该模型进行在线故障检测，实际使用证明该模型具有良好的收敛性和稳定性，完全能满足对气体传感器故障在线检测的需要。     

Formulation of bead width model of an SLM prototype using modified multi-gene genetic programming approach

The rapid prototyping (RP) processes, specifically selective laser melting (SLM), are popular for building complex 3D parts directly from the metal powder. The literature reveals that the properties such as surface roughness, waviness, bead width, compressive strength, tensile strength, wear, and dimensional accuracy of an SLM-fabricated prototype depend on the parameter settings of the SLM setup and can be improved by appropriate adjustment. For the selection of an optimal parameter setting, multi-gene genetic programming (MGGP), which develops the model structure and its coefficients automatically, can be applied. The model participating in the evolutionary stage of the MGGP method is a linear weighted sum of several genes (model trees) regressed using the least squares method. In this combination mechanism, the occurrence of gene of lower performance in the MGGP model can degrade its performance. This paper proposes a modified MGGP (M-MGGP) method using a stepwise regression approach such that the genes of lower performance are eliminated, and only the high performing genes are combined. The M-MGGP approach is applied on the bead width data obtained from the experiments conducted on the SLM machine, and its performance is found to be better than that of the standardized MGGP and artificial neural network (ANN) models. Between MGGP and ANN, ANN has shown better performance. Further, the parametric and sensitivity analysis conducted validates the robustness of our proposed model and is proved to capture the dynamics of the SLM process by unveiling important process parameters and the hidden non-linear relationships.