基于自适应参数估计的三轴磁传感器实时校正方法

针对传统基于椭球模型三轴磁传感器无法实时校正的问题,设计了基于自适应参数估计的三轴磁传感器实时校正方法,通过对参数的实时估计与处理,实现了三轴磁传感器误差的校正。首先,对三轴磁传感器全误差模型进行分析与处理,建立了基于模值的参数估计方程;其次,详细分析了参数模型的噪声特性,针对非高斯状态相关噪声,提出了采用自适应参数估计方法;最后,设计仿真与实验分析,完成三轴磁传感器误差校正。实验结果表明,所提出的算法可以实现误差参数的实时校正,提升了磁传感器误差校正的自主性。同时,基于自适应参数估计方法,使得校正结果模值标准差相较于传统方法减小了3倍。     

不受姿态误差影响的惯性/磁感应融合定位方法

针对传统电磁信标导航定位系统中由惯性元件解算磁传感器姿态误差导致定位性能下降问题，提出了一种不受传感器姿态影响的改进惯性/磁感应定位融合定位方法，避免了姿态误差对定位结果的影响。首先，基于磁信标磁场分布规律与特征矢量构建了与传感器姿态无关的非线性磁感应定位模型；然后，结合惯性元件对载体目标的状态估计提出了改进的惯性/磁感应定位模型，降低了累计误差、提高了定位精度与结果的输出速度；最后，利用无迹卡尔曼滤波（UKF）对融合数据进行处理，实现对动态目标的位置估计。分别通过实验和仿真验证了不受姿态影响的磁感应定位模型以及惯性/磁感应定位方法的有效性。实验结果表明磁感应定位模型能够不受传感器姿态误差的影响实现定位；数值仿真结果表明惯性元件解算姿态辅助磁信标定位方法的最大误差为7.29 m，基于改进的惯性/磁感应融合定位方法最大误差0.77 m。     

基于椭球拟合的磁梯度张量系统集成校正

为了提高磁梯度张量系统测量精度,针对磁传感器轴位偏差、标度因子与非正交性,传感器阵列间非对准误差以及载体磁干扰等误差因素,提出了基于椭球拟合的磁梯度张量系统集成校正方法。根据硬、软磁材料磁场数学模型,将磁传感器系统误差和硬、软磁干扰误差构建为集成误差系数矩阵和集成零漂向量两部分,基于最小二乘椭球拟合建立了集成补偿模型以此校正传感器输出。通过正交系间旋转矩阵构建非对准参数的线性方程组并得到最小二乘估计,以此对准张量系统。研究结果表明:仿真校正后传感器总磁场强度(TMI)和系统张量分量有效收敛于预设测量噪声;实验校正后各传感器输出具有较高重合与同轴性,总磁场强度均方根误差(RMSE)降至约5nT,张量分量均方根误差限制在20nT/m内。该方法能够稳定可靠地提高磁梯度张量系统测量精度。     

惯性-视觉-磁场传感器组合的低成本标定方法

针对一种融合了惯性-视觉-磁场信息的传感器组合,给出了一套低成本的标定和建模方法.对于传感器模型中的确定性项和非确定性项参数,分别采用了基于输入指令向量模匹配的方法和基于时间序列分析的方法来进行标定和建模,利用所得到的模型补偿之后的传感器输出误差的方差降为原来的1/3以下;对于摄像机坐标系和加速度计坐标系之间的相对旋转矩阵,提出了一种基于重力向量和铅垂线的标定方法,仿真结果表明,标定结果的相对误差小于7%.     

姿态检测地磁传感器误差分析与补偿方法

针对磁环境下的地磁传感器测量精度不足的问题,为了补偿地磁传感器存在的各种误差,在分析地磁传感器的自身误差和环境干扰误差的基础上,建立了地磁传感器误差校正模型,利用最大似然估计法将误差校正模型转化成多项式最小二乘拟合的基本方程形式,提出了基于QR因子分解法的补偿系数快速解算方法。分别求取仿真误差模型和实验误差模型的补偿系数,然后对地磁场矢量进行校正。结果表明,校正后的磁场强度和实际磁场强度基本一致,滚转角精度提高了近5倍,能够满足相关需求。     

基于偏振光及红外传感器的辅助定姿方法

为了满足无人飞行器自主导航需求,提出了一种偏振光及红外传感器辅助惯导定姿方法。该方法选取了姿态四元数和陀螺误差模型构建滤波状态向量,采用偏振光传感器、红外传感器测量的三维姿态角作为量测向量建立卡尔曼滤波模型;融合后的最优姿态四元数转换为姿态矩阵反馈至惯性导航系统中,修正位置、姿态信息的解算。在分析了捷联惯导力学编排的基础上,参考常见的惯导系统参数进行了动态仿真。结果表明:偏振光、红外传感器可以有效地修正惯导姿态误差,修正后输出精度与偏振光、红外传感器输出精度相当,分别保持在±0.2°和±1°。提出的新型辅助定姿方法为提高惯导系统精度提供了一种新思路,具有广阔的应用前景。     

激光陀螺捷联惯性组合的全温度标定方法

给出了激光陀螺捷联惯性组合(IMU)的误差模型，研究了一种利用双轴带温控箱速率转台的参数标定方法，标定出了IMU在各种环境温度下的模型参数，通过温度补偿有效地减小了IMU的导航误差。试验结果表明。该方法标定精度较高，适用于中等精度IMU的参数标定。     

一种基于神经网络的传感器故障诊断方法

提出了基于两级神经网络结构的多传感器斜置组件的故障诊断方法,以消除传感器安装误差、刻度系数误差以及常值偏差对故障检测与隔离准确性的影响。与基于参数估计的奇偶向量补偿方法相比,这种方法不需要各项误差的动态模型和噪声的统计特性。     

一种石英挠性加速度计非线性误差系统级标定方法

为提高大过载高动态环境下捷联惯导系统导航精度，需对捷联惯导系统中的石英挠性加速度计非线性误差参数进行精确标定。针对现有标定方法在加速度计非线性误差参数发生变化时无法满足免拆卸高精度标定的问题，设计了一种基于双轴精密离心机和捷联惯导系统转位机构交替旋转、依靠转位机构实现9位置标定路径的系统级标定方法。经理论分析和仿真验证，所提方法可实现加速度计二次项、交叉耦合项共九个非线性误差参数系统级高精度标定，二次项误差参数标定精度优于1.0×10^-6 g/g²，交叉耦合项误差参数标定精度优于1.5×10^-6 g/g²。     

有限空间爆炸静态压力的温度补偿方法

为改善压阻式压力传感器的温度漂移特性，构建了基于遗传算法和小波神经网络的压力传感器温度补偿模型。针对小波神经网络收敛速度慢且易陷入局部最优解的问题，采用遗传算法对小波神经网络的连接权值、伸缩参数和平移参数进行优化。基于压力传感器的标定数据，分别采用BP神经网络、小波神经网络和遗传小波神经网络对其进行温度补偿研究，结果表明:遗传小波神经网络兼容了小波分析的时频局部特性和神经网络的自学习能力，表现出良好的收敛速度和补偿精度，经补偿后传感器的输出值更接近于标定值，其最大误差由?17.44 kPa变至0.38 kPa，最大相对误差由?14.0%变至0.38%。将该模型应用于有限空间爆炸静态压力的温度补偿中，取得了较好的实际应用效果。     

Parameter estimation,data compression and stochastic noise elimination in robotics: a wavelet domain-based integrated approach

Accurate parameter estimation in presence of stochastic noise is the essential part of almost all control hardware of the plants. However, the optimal design of the control hardware depends on processing power and installed memory. The proposed research investigation focuses on precise parameter estimation from compressed temporal data of error dynamics with exiguous susceptibility to the robot’s controllability. Instead of using Maximum Likelihood estimation (MLE) and Least Squares (LS) estimation in the time domain, the proposed method exploits the recursive wavelet domain’s properties to selectively store the error data coefficients negating the data related to noise. As a result, data compression is achieved. The proposed algorithm may be directly implemented on any scalable “Very Large Scale Integration” (VLSI) circuit due to the recursive implementation. For the evaluation of robustness, dynamic parameter variation is considered. The variation in scalar & vector-valued error is considered to evaluate the performance of the stochastic system. The proposed algorithm implementation is demonstrated experimentally on commercially available Omni Bundle robot.

     

Long-memory recursive prediction error method for identification of continuous-time fractional models

This paper deals with recursive continuous-time system identification using fractional-order models. Long-memory recursive prediction error method is proposed for recursive estimation of all parameters of fractional-order models. When differentiation orders are assumed known, least squares and prediction error methods, being direct extensions to fractional-order models of the classic methods used for integer-order models, are compared to our new method, the long-memory recursive prediction error method. Given the long-memory property of fractional models, Monte Carlo simulations prove the efficiency of our proposed algorithm. Then, when the differentiation orders are unknown, two-stage algorithms are necessary for both parameter and differentiation-order estimation. The performances of the new proposed recursive algorithm are studied through Monte Carlo simulations. Finally, the proposed algorithm is validated on a biological example where heat transfers in lungs are modeled by using thermal two-port network formalism with fractional models.

     

基于一种免疫算法的结构动态参数识别

受生物免疫系统的启发提出了一些基于免疫概念的优化算法。该方法有效地避免了传统遗传算法易于出现早熟、搜索效率低及不能很好保持个体多样性等问题。本文将免疫算法应用于结构动态参数识别领域，通过其在VC环境下调用ANSYS有限元程序中的APDL语言，并在后期引入加速循环操作，从而实现了对结构动态参数快速而精确的识别，仿真算例表明其正确性、有效性和较好的抗观测噪声能力。该方法特别适合于对大型复杂结构的参数识别计算。     

A new analytical formula to compute the step length of Padé approximants in the ANM: Application to buckling structures

In this paper, we propose a new analytical formula to define the next branch in the Asymptotic Numerical Method (ANM) using the Padé approximants. The proposed formula is based on the computation of the relative error of two consecutive Padé approximants. This formula is obtained by developing the relative error with respect to the path parameter. An appropriate matrix formulation is adopted for the computation of this relative error. A comparison between the analytical formula proposed in this paper and the classical continuation Padé approximants using the step length computed numerically using dichotomy method is presented for examples of buckling structures.     

Self-adaptive one-dimensional nonlinear finite element method based on element energy projection method

The element energy projection （EEP） method for computation of super- convergent resulting in a one-dimensional finite element method （FEM） is successfully used to self-adaptive FEM analysis of various linear problems, based on which this paper presents a substantial extension of the whole set of technology to nonlinear problems. The main idea behind the technology transfer from linear analysis to nonlinear analysis is to use Newton＇s method to linearize nonlinear problems into a series of linear problems so that the EEP formulation and the corresponding adaptive strategy can be directly used without the need for specific super-convergence formulation for nonlinear FEM. As a re- sult, a unified and general self-adaptive algorithm for nonlinear FEM analysis is formed. The proposed algorithm is found to be able to produce satisfactory finite element results with accuracy satisfying the user-preset error tolerances by maximum norm anywhere on the mesh. Taking the nonlinear ordinary differential equation （ODE） of second-order as the model problem, this paper describes the related fundamental idea, the imple- mentation strategy, and the computational algorithm. Representative numerical exam- ples are given to show the efficiency, stability, versatility, and reliability of the proposed approach.     

基于Kriging模型和改进MCMC算法的随机有限元模型修正

针对待修正参数维数较高时,标准马尔可夫链蒙特卡罗MCMC (Markov Chain Monte Carlo)算法不易收敛、拒绝率高的问题,提出了基于Kriging模型和在MCMC中融合花朵授粉算法的修正方法.首先,以待修正参数作为输入,以应变模态作为输出,建立Kriging模型,通过蝙蝠算法确定Kriging模型的相关系数;然后,采用最大熵的贝叶斯方法估计参数的后验概率密度函数,将花朵授粉算法融入MH (M etropolis-Hasting)抽样算法,提高局部寻优和全局寻优能力;最后,通过三自由度弹簧-质量系统和三维桁架结构的数值算例验证所提模型修正方法,修正后参数相对误差均低于0.86％.结果 表明,所提方法修正后较高维参数的马尔可夫链能够快速收敛且样本接受率也有所提高,该方法也对随机噪声具有一定的鲁棒性.     

基于雷达测量数据的弹箭扰动源最优估计

为精确计算舰载武器的飞行状态参数,以弹体纵向运动过程为研究对象,推导了包含误差干扰源在内的纵向扰动运动学方程,利用"系数冻结法"及拉普拉斯变换得到解析解,拟合出弹体被动段纵向运动的气动参数公式,采用三坐标雷达测量量作为系统量测方程,从而对气动参数误差干扰源进行最优估计。计算结果表明:该算法可以使俯仰操纵力矩系数导数误差的精度稳定在±0.007(°)~(-1)范围内,阻力系数误差的精度趋于±0.025之间;升力系数误差基本稳定在±0.12之间;俯仰力矩系数对攻角的导数偏差精度稳定在±0.011(°)~(-1)之间;俯仰阻尼力矩系数误差的精度收敛于±0.009(°)~(-1)之间,且算法收敛速度快,可为重构高精度的弹道参数提供支撑。     

A modified time domain subspace method for nonlinear identification based on nonlinear separation strategy

Nonlinear factors existing in engineering structures have drawn considerable attention, and nonlinear identification is a competent technique to understand the dynamic characteristics of nonlinear structures. Therefore, in this paper, a novel nonlinear separation subspace identification (NSSI) algorithm based on subspace algorithm and nonlinear separation strategy is proposed to conduct nonlinear parameter identification of nonlinear structures. For the proposed NSSI algorithm, the low-level excitation test is firstly conducted to obtain the transfer matrix in the linear response formula. Then, the obtained transfer matrix is used in the high-level excitation test to calculate the nonlinear response part by the proposed nonlinear separation strategy, and the subspace algorithm is utilized to identify the nonlinear parameter on the modified state-space model including only the nonlinear part. The proposed NSSI algorithm can reduce the coupling error caused by simultaneously processing both the large number part (corresponding to the linear part) and small number part (corresponding to the nonlinear part) in the traditional nonlinear subspace identification (NSI) algorithm. At last, two numerical experiments are given to validate the effectiveness of the developed novel nonlinear identification method. Furthermore, some influence factors are discussed to show the stability of the identification algorithm, and some comparisons between the proposed NSSI method and traditional NSI method are also conducted to demonstrate the advantages of the novel method.     

基于Bagging模型的惯导系统误差抑制方法

由惯性导航系统(SINS)和卫星导航系统(GPS)构成的组合导航系统一直是陆用车辆的主要导航设备。当GPS失锁时,SINS的定位误差将随着时间不受控制的迅速增长。为了提高惯导系统的定位精度,相比较于单一的神经网络,集成学习算法中的Bagging模型能够深度学习惯导误差之间的内在关系,进一步提高导航性能。在智能算法和组合导航系统的框架下提出了惯导系统的误差抑制方案,即在GPS存在时训练组合导航系统数据,当GPS失锁时预测惯导系统位置增量。试验结果表明,该方案能够在GPS丢失时抑制惯导系统定位误差发散,相比较于BP算法,Bagging模型的定位精度在5 min时提高了约49%,15 min时提高了约41%。     

SELF-ADAPTIVE STRATEGY FOR ONE-DIMENSIONAL FINITE ELEMENT METHOD BASED ON ELEMENT ENERGY PROJECTION METHOD

Based on the newly-developed element energy projection (EEP) method for computation of super-convergent results in one-dimensional finite element method (FEM), the task of self-adaptive FEM analysis was converted into the task of adaptive piecewise polynomial interpolation. As a result, a satisfactory FEM mesh can be obtained, and further FEM analysis on this mesh would immediately produce an FEM solution which usually satisfies the user specified error tolerance. Even though the error tolerance was not completely satisfied, one or two steps of further local refinements would be sufficient. This strategy was found to be very simple, rapid, cheap and efficient. Taking the elliptical ordinary differential equation of second order as the model problem, the fundamental idea, implementation strategy and detailed algorithm are described. Representative numerical examples are given to show the effectiveness and reliability of the proposed approach.