状态变换卡尔曼滤波的进一步解释及应用

针对速度误差定义的坐标系一致性,对状态变换卡尔曼滤波(ST-EKF)的合理性作出了进一步的解释,并将其应用于捷联惯导的初始对准及组合导航。为提高陀螺仪、加速度计的零偏误差估计效果,初始对准和组合导航过程采用统一的15状态滤波器。单位置精对准实验表明,由于在基座振动的环境下误差状态不完全可观,传统卡尔曼滤波会有随时间增长航向角估计误差反而逐渐发散的情况出现,而状态变换卡尔曼滤波的航向角估计结果相对稳定。激光陀螺IMU和光纤陀螺IMU的3 min双位置对准实验均表明,在同样的卡尔曼滤波预测和更新频率下,状态变换卡尔曼滤波的航向角估计精度要比传统卡尔曼滤波提高45%以上。激光陀螺IMU/卫星和MEMS-IMU/卫星组合导航跑车实验均表明,多处断开卫星信号的情况下,状态变换卡尔曼滤波组合导航系统具有更高的定位精度。     

基于状态观测器的扰动补偿在转台控制中的应用

为了减小干扰力矩对转台性能的影响,提出了基于观测器的扰动补偿控制来抑制干扰力矩的方法.首先采用传递函数的方法分析了状态观测器中各观测状态与观测器输入之间的关系,利用观测器获得角加速度信号构造扰动补偿器对外部扰动进行补偿控制,设计了具有扰动补偿的转台控制系统,并分析了加入扰动补偿后对控制系统的影响.分析和试验结果表明,此基于状态观测器的扰动补偿可以对低频的扰动进行有效的抑制,能够显著的提高转台低频小信号振动时的失真度和三轴联动精度,同时对瞬态扰动也有一定的抑制作用.     

基于扩展卡尔曼滤波的转速补偿滚转角测量算法

针对二维弹道修正引信全弹道转速变化范围较大的旋转特点,提出了一种基于扩展卡尔曼滤波的转速补偿滚转角测量算法。首先以某双旋稳定迫弹为仿真平台,分别在高速和低速时变转速条件下对引入转速补偿的EKF滚转角测量算法进行了验证。仿真结果表明,高转速条件下全弹道滚转角解算绝对误差不大于5°,低转速条件下解算绝对误差全弹道不大于2.5°,且均能快速收敛。而后又基于MEMS三轴转台进行了实验室滚转角测量精度验证,结果表明,在修正引信转速自30 r/s至1 r/s范围动态变化过程中,滚转角解算绝对误差不超过4°。     

基于状态反馈的卡尔曼滤波组合导航算法

在卡尔曼滤波算法的基础上,提出一种导航误差状态估计的新方法。该方法选取SINS和GPS三个方向上的速度差和位置差作为观测量,根据惯性器件和GPS的精度确定噪声强度,同时建立反馈控制与卡尔曼滤波相结合的滤波器,最终得到误差状态的估计值。仿真结果表明,新型滤波器不仅解决了卡尔曼滤波发散的问题,而且使得误差估计精度得到了极大的提高,从而为组合导航系统的误差估计技术找到了更有工程应用价值的方法。     

基于扩展卡尔曼滤波的X射线脉冲星相对导航方法

X射线脉冲星作为一种天然时间基准,具有良好的周期稳定性,在深空探测航天器自主导航方面具有重要的应用前景。针对绝对导航的整周模糊度求解问题和相对导航的预报相位、标称轨道存储问题,提出了一种改进的相对导航方法,将前一时刻的航天器位置作为相对导航基准,建立航天器轨道动力学模型,采用扩展卡尔曼滤波算法,确定航天器的位置。最后,进行了数学仿真研究,仿真结果表明:脉冲星相对导航改进方案的导航精度达到1 km,滤波器一致渐进稳定,能够快速抑制初始误差和轨道根数的干扰,验证了导航方案的正确性和可行性。     

Optimal bounded control for nonlinear stochastic smart structure systems based on extended Kalman filter

An optimal bounded control strategy for smart structure systems as controlled Hamiltonian systems with random excitations and noised observations is proposed. The basic dynamic equations for a smart structure system with smart sensors and actuators are firstly given. The nonlinear stochastic control system with noised observations is then obtained from the simplified smart structure system, and the system is expressed by generalized Hamiltonian equations with control, random excitation and dissipative forces. The optimal control problem for nonlinear stochastic systems with noised observations includes two parts: optimal state estimation and optimal response control based on estimated states, which are coupled each other. The probability density of optimally estimated systems has generally infinite dimensions based on the separation theorem. The proposed optimal control strategy gives an approximate separate solution. First, the optimally estimated system state is determined by the observations based on the extended Kalman filter, and the estimated nonlinear system with controls and stochastic excitations is obtained which has finite-dimensional probability density. Second, the dynamical programming equation for the estimated system is determined based on the stochastic dynamical programming principle. The control boundedness due to actuator saturation is considered, and the optimal bounded control law is obtained by the programming equation with the bounded control constraint. The optimal control depends on the estimated system state which is determined by noised observations. The proposed optimal bounded control strategy is finally applied to a single-degree-of-freedom nonlinear stochastic system with control and noised observation. The remarkable vibration control effectiveness is illustrated with numerical results. Thus the proposed optimal bounded control strategy is promising for application to nonlinear stochastic smart structure systems with noised observations.     

基于广义卡尔曼滤波的桥梁结构物理参数识别

基于广义卡尔曼滤波提出了随机荷载作用下桥梁结构物理参数的识别方法。首先,以荷载为观测对象,推导出基于有限元模型的桥梁结构系统的观测方程,以结构待识别的物理参数为状态向量,建立系统状态方程;然后,对该状态方程和观测方程构成的非线性参数系统应用广义卡尔曼滤波,从而识别出结构的物理参数。对一座简支梁桥和一座三跨连续梁桥在不同工况下的物理参数识别进行了数值仿真,结果表明本文方法能够准确地识别桥梁结构全部刚度参数、质量参数和阻尼参数,且具有很强的抗噪性能,从而验证了本文方法的有效性和鲁棒性,可应用于识别大型桥梁结构的物理参数。     

基于扩展卡尔曼滤波器的INS/WSN无偏紧组合方法(英文)

为了减少传统组合导航方法中舍去泰勒展开式的高次项对导航精度的影响,提出了一种基于扩展卡尔曼滤波的INS/WSN无偏紧组合方法。这种方法采用INS在本地相对坐标系的位置和速度误差作为系统变量,观测方程中未知节点理想的位置和速度被INS测量得到的位置和速度与其预估误差的差值替代。仿真结果显示,本文提出的方法平均位置误差比松组合方法降低20%左右,平均速度误差比松组合方法降低80%左右。     

Identification of a fault zone ahead of the tunnel excavation face using the extended Kalman filter

Simulation of mechanized tunneling and on-site excavation require very good knowledge of the geomechanical and material properties. Identification of the material must be fast and continuously performed during tunnel excavation for the best possible strategies for advancing the tunnel boring machine. We present in this work the use of the extended Kalman filter (EKF) for identification of the inclined fault zone ahead of the face. The EKF showed fast and stable convergence of the model parameters under study. In comparison with the particle swarm optimization technique applied to the same back analysis problem, faster convergence of the identified parameters as well as high robustness with respect to the choice of the initial parameter values have been observed.     

Intermittent observer for nonlinear systems with state delays

Kolmogorov–Sinai entropy-based irregularity measures such as approximate entropy (ApEn), sample entropy and fuzzy entropy are widely used for short-term heart rate variability analysis. These entropy statistics are estimated for a specific value of the tolerance parameter (r) that is mostly chosen from a common recommended range. Entropy measurement on short-term signals is highly sensitive to the choice of r. An incorrect selection of r results in an inaccurate entropy value, thereby leading to unreliable information retrieval. By addressing this inaccuracy due to r selection, the quality and reliability of information retrieval can be improved. Thus, we hypothesize that generating a complete entropy profile using all potential r values will give a more complete and useful information about signal irregularity in contrast to the case of finding entropy at a single selected value of r. In order to do so, one must be able to accurately select all potential r candidates. In this paper, we use a data-driven algorithm based on cumulative histograms to automatically select potential r values for an individual signal based on its dynamics. An appropriate set of r values is designated by the algorithm for generating a series of ApEn values (ApEn profile) instead of a single value of ApEn. ApEn profile- based secondary measures such as TotalApEn and SDApEn have been used as features to classify sets of synthetic and physiologic data. Our study proves that secondary measures obtained from an ApEn profile are more efficient in indicating irregularity levels in comparison with the traditional measure of ApEn evaluated at a single r value, specially in the case of short length data.     

状态变换扩展卡尔曼平滑算法在AUV水下航迹修正中的应用

水下自主潜航器(AUV)在水下勘测过程中常采用定期上浮的方式利用卫星定位信息(GNSS)校正惯性/多普勒测速仪(SINS/DVL)组合导航造成的位置误差,并通过平滑算法校正之前计算的水下航迹。针对AUV上浮前的水下航迹矫正问题,提出一种基于计算地理坐标系的状态变换扩展卡尔曼平滑算法(STEKF-RTS)。首先推导状态变换扩展卡尔曼滤波器的系统误差状态方程,并分别以GNSS信息和DVL速度为观测信息构建量测方程,以此系统模型进行前向滤波。完成前向滤波后,即潜航器上浮GNSS校正完成后,采用RTS平滑算法对水下航行阶段的航迹进行修正。实验结果表明,STEKF相较于EKF能减缓位置误差积累,STEKF-RTS相比EKF-RTS平滑算法能够进一步有效校正航线误差,1 h上浮时间间隔情况下,勘测航迹精度可控制在0.5%D以内。     

Online identification of time-variant structural parameters under unknown inputs basing on extended Kalman filter

Nonlinear Dynamics - To date, many parameter identification methods have been developed for the purpose of structural health monitoring and vibration control. Among them, the extended Kalman filter...     

基于修正的Rodrigues参数的EKF算法在姿态估计中的应用

以低成本航姿测量系统为研究对象,针对四元数描述姿态时存在冗余的不足,提出使用三维独立矢量的修正的Rodrigues参数进行姿态解算,利用与其影子参数相互切换的方法解决了修正的Rodrigues参数在描述姿态时存在奇异的问题,根据系统中器件的特点,以陀螺零偏和修正的Rodrigues参数为状态向量,以加速度计、磁强计输出为观测向量建立了基于扩展Kalman滤波的姿态估计算法,避免了利用四元数进行姿态估计时状态误差方差阵产生奇异的问题,并在一定程度上减小了估计算法的计算量。最后通过全姿态以及摇摆基座数据仿真验证了算法的有效性。     

Super-twisting sliding mode control for aircraft at high angle of attack based on finite-time extended state observer

Nonlinear Dynamics - This paper proposes a finite-time decoupling control strategy for aircraft with thrust vector at high angle of attack maneuver. Firstly, the nonlinear mathematical model of the...     

降阶扩展卡尔曼滤波算法在船用INS/GPS组合导航系统中的应用

TNNS(真航向导航系统)由MS860接收机、INS及处理数据的PC/104架构的嵌入式工控机构成。针对TNNS推导了INS(惯性导航系统)的误差模型,提出了适合于TNNS的降阶扩展卡尔曼滤波算法组合GPS和INS。系统在东海作了三次海试,软件及滤波算法平台由C/C 编制。海上试验表明,组合滤波后,INS的位置误差由100m降低到40m以下;进行最优化滤波后的航向误差σ由原来的0.105°减小为0.034°,纵横摇的误差也大幅减小。整个海试结果表明,在TNNS中组合GPS/INS采用的降阶扩展卡尔曼滤波算法,大幅提高了系统精度和可靠性。     

Adaptive neural impedance control for electrically driven robotic systems based on a neuro-adaptive observer

Nonlinear Dynamics - This paper proposes an adaptive neural impedance control (ANIC) strategy for electrically driven robotic systems, considering system uncertainties and external disturbances....     

Disturbance observer based fault-tolerant control for cooperative spacecraft rendezvous and docking with input saturation

Nonlinear Dynamics - A robust nonlinear control strategy is presented for a cooperative spacecraft rendezvous and docking maneuver, where the pursuer spacecraft is subject to input saturation and...     

基于单向无迹卡尔曼滤波的飞机姿态算法

针对扩展卡尔曼滤波方法在飞机姿态滤波中存在的线性化误差大、需要解繁琐的Jacobian矩阵等问题,将一种新型卡尔曼滤波方法——单向无迹卡尔曼滤波应用于载有低精度、高噪声传感器的低成本飞机姿态滤波系统,并在精度及计算量上与EKF和容积卡尔曼滤波进行了比较。利用实测飞行数据进行实验,结果表明:相对于EKF,SUKF实现容易,且使姿态滤波精度提高到二阶;相对于CKF,SUKF计算简单,比CKF减少约33%的计算量。     

Sliding mode control with an extended disturbance observer for a class of underactuated system in cascaded form

Nonlinear Dynamics - A sliding mode controller based on an extended disturbance observer is investigated to control a class of underactuated system in this paper. By using strict feedback...     

光电跟踪模型噪声卡尔曼滤波算法

为减小滤波性能对跟踪目标状态空间模型噪声的敏感性,提出一种基于新息同时更新系统噪声方差和测量噪声方差方法,并将其与非线性卡尔曼滤波类算法相结合,构成一类适用于光电跟踪目标的自适应非线性卡尔曼滤波算法.同时将此方法应用到非线性测量光电跟踪系统中,并与扩展卡尔曼滤波和U卡尔曼滤波进行性能对比.仿真实验结果证明该方法可以实时调整系统噪声方差和测量噪声方差,有效地避免由于系统模型噪声统计特性不准确所带来的滤波性能下降的问题,而且其性能明显优于扩展卡尔曼滤波和U卡尔曼滤波.