一类非线性系统的D型在线学习控制

结合对初始状态的学习,给出了一类输出方程不含控制变量的时变非线性系统的D型在线学习控制算法及其收敛条件。其特点是将学习误差在线反馈,且迭代初始状态只需通过理想信号及系统的部分信息即可确定。最后,将所得结论应用于机器人系统,表明本文方法是有效的。     

基于IGA-ELM网络的滚动轴承故障诊断

为了提高航空发动机轴承故障诊断准确率,提出基于改进遗传算法优化极限学习机网络（IGA-ELM）的诊断模型。针对传统遗传算法易早熟等缺陷,对遗传算法的交叉操作和变异操作进行改进,并用改进的遗传算法优化极限学习机的输入权值矩阵和隐含层阈值,利用Moore-Penrose算法计算极限学习机的输出权值矩阵。使用IGA-ELM诊断模型对滚动轴承正常、内环故障、外环故障和滚珠故障4种工况进行诊断,并分析极限学习机隐含层神经元的数量和激活函数对轴承故障诊断的影响。为了验证改进遗传算法优化极限学习机的有效性,将传统遗传算法、自适应遗传算法和粒子群算法作为对比算法。经过分析表明：改进遗传算法收敛速度和收敛误差,均优于对比算法。     

准实时性前斜视INS/SAR组合导航滤波算法研究

为消除常规利用正侧视SAR图像辅助导航信息对惯性误差进行修正时的滞后影响,结合前斜视SAR成像工作特性,本文分析提出了新的准实时性组合导航滤波方案和算法,以解决SAR/INS组合导航系统中的量测滞后影响;本文分析了前斜视SAR成像辅助导航系统工作特点,分析了SAR量测输出模型;建立了准实时性前斜视INS/SAR组合导航滤波算法模型,并通过仿真分析了组合导航系统性能。仿真结果表明：采用所提出的惯性/SAR组合方案和算法,可以有效克服成规正侧视SAR辅助匹配输出滞后的影响,有效满足组合系统性能要求。     

A novel variable structure multi-model approach based on error-ambiguity decomposition

Model Set Adaptation (MSA) plays a key role in the Variable Structure Multi-Model tracking approach (VSMM). In this paper, the Error-Ambiguity Decomposition (EAD) principle is adopted to derive the EAD-MSA criterion that is optimal in the sense of minimizing the square error between the estimate and the truth. Consequently, the EAD Variable Structure first-order General Pseudo Bayesian (EAD-VSGPB1) algorithm and the EAD Variable Structure Interacting Multiple Model (EAD-VSIMM) algorithm are constructed. The proposed algorithms are tested in two groups of maneuvering target tracking scenarios under different modes and observation error conditions. The simulation results demonstrate the effectiveness of the EAD-VSMM approach and show that, compared to some existing multi-model algorithms, the proposed EAD-VSMM algorithms achieve more robust and accurate tracking results.     

一种非线性滤波器的误差分析及应用

针对一类具有未建模误差和扰动的非线性系统的状态估计问题,提出一种在线估计并补偿模型误差的非线性滤波算法,该算法利用非线性预测滤波(NPF)基于预测输出残差的方差最小的基本原则估计模型误差,冉利用扩展卡尔曼滤波(EKF)的思想对补偿后的模型进行状态估计;详细推导了状态估计误差及其方差阵的传播模型.以卫星姿态确定系统为例,...     

Geometrical entropy approach for variable structure multiple-model estimation

The variable structure multiple-model(VSMM) estimation approach, one of the multiple-model(MM) estimation approaches, is popular in handling state estimation problems with mode uncertainties.In the VSMM algorithms, the model sequence set adaptation(MSA) plays a key role.The MSA methods are challenged in both theory and practice for the target modes and the real observation error distributions are usually uncertain in practice.In this paper, a geometrical entropy(GE) measure is proposed so that the MSA is achieved on the minimum geometrical entropy(MGE) principle.Consequently, the minimum geometrical entropy multiple-model(MGEMM) framework is proposed, and two suboptimal algorithms, the particle filter k-means minimum geometrical entropy multiple-model algorithm(PF-KMGEMM) as well as the particle filter adaptive minimum geometrical entropy multiple-model algorithm(PF-AMGEMM), are established for practical applications.The proposed algorithms are tested in three groups of maneuvering target tracking scenarios with mode and observation error distribution uncertainties.Numerical simulations have demonstrated that compared to several existing algorithms, the MGE-based algorithms can achieve more robust and accurate estimation results when the real observation error is inconsistent with a priori.     

基于微小卫星的空间目标光学探测定位方法

为实现空间目标的天基光学观测,利用双星定位原理,采用仅测角的观测方式,结合最小二乘法建立了双星几何定位模型。考虑到星点提取作为光学定位算法中的重要一环将直接影响最终的定位精度,因而针对就星图处理部分,分析了星图的灰度特点,采用阈值分割法进行了去噪处理,选取了几种质心法对星点进行提取,利用真实星图对提取结果进行比较,选出了形心法和阈值质心法 2种较为可行的星点提取算法。通过仿真计算和蒙特卡洛误差分析结果可知,不考虑误差时,定位误差小于 10-5 m;考虑主要误差源时,定位误差小于 100 m;且姿态误差对定位方法的精度影响最大,数值上可以达到总误差的 0.9倍以上,速度误差的影响最小,只有总误差的 1/30,可以忽略;定位误差近似呈高斯分布且误差的大小与误差源近似呈线性关系。由此可见,文章所建立的定位算法可以对空间目标实现较为精确的定位。     

多输入模糊控制器及其在飞行控制中的应用

针对输入量之间具有导数关系的三输入模糊控制器进行了理论分析。通过输入空间分区和非线性反模糊算法的引入,推导出了三输入模糊控制器的有关算法,并且总结出它与传统ＰＩＤ控制器之间的等价性及等价条件。飞机纵向运动仿真计算结果表明：三输入模糊控制器与二输入模糊控制器相比较,当初始状态变化较大时,对高度和速度的控制更有效。     

Adaptive robust cubature Kalman filtering for satellite attitude estimation

This paper is concerned with the adaptive robust cubature Kalman filtering problem for the case that the dynamics model error and the measurement model error exist simultaneously in the satellite attitude estimation system. By using Hubel-based robust filtering methodology to correct the measurement covariance formulation of cubature Kalman filter, the proposed filtering algorithm could effectively suppress the measurement model error. To further enhance this effect and reduce the impact of the dynamics model error, two different adaptively robust filtering algorithms, one with the optimal adaptive factor based on the estimated covariance matrix of the predicted residuals and the other with multiple fading factors based on strong tracking algorithm, are developed and applied for the satellite attitude estimation. The quaternion is employed to represent the global attitude parameter, and three-dimensional generalized Rodrigues parameters are introduced to define the local attitude error. A multiplicative quaternion error is derived from the local attitude error to maintain quaternion normalization constraint in the filter. Simulation results indicate that the proposed novel algorithm could exhibit higher accuracy and faster convergence compared with the multiplicative extended Kalman filter, the unscented quaternion estimator, and the adaptive robust unscented Kalman filter.     

Radar target tracking-Viterbi versus IMM

The performance of the Viterbi and the interacting multiple model (IMM) algorithms, applied to radar tracking and detection, are investigated and compared. Two different cases are considered. In the first case, target acceleration is identical to one of the system models while in the second case it is not. The performance of the algorithms depends monotonically on the maximal magnitude of the difference between models acceleration, on the time interval between measurements, and on the reciprocal of the measurements error standard deviation. In general, when these parameters are relatively large, both algorithms perform well. When they are relatively small, the Viterbi algorithm is better. However, during delay periods, namely right after the start of a maneuver, the IMM algorithm provides better estimations     

面向声源定位的改进广义互相关时延估计方法

面向以数字微机电系统(MEMS)麦克风作为声传感器的声源定位阵列,完成了前端麦克风阵列的电路设计和以现场可编程逻辑门阵列(FPGA)为核心的数据采集系统的开发,并对麦克风输出的脉冲密度调制信号进行了降采样处理.针对传统的广义互相关算法在低信噪比下时延估计误差较大的问题,提出了一种改进PHAT加权函数的方法.在同等条件下对基于不同加权函数的广义互相关算法进行了MATLAB仿真验证,实验结果表明,在低信噪比条件下该方法相较于传统的广义互相关算法,时延估计误差更小且抗噪性能更强.     

Error estimation of INS ground alignment through observabilityanalysis

A systematic analysis of the observability of an inertial navigation system (INS) in ground alignment with Bar-Itzhack and Berman's error model is presented. It is shown that the unobservable states are separately contained in two decoupled subspaces. The constraints on the selection of unobservable states are discussed. An estimation algorithm which is derived fully from the horizontal velocity outputs for computing the misalignment angles is provided. It reveals that the azimuth error can be entirely estimated from the estimates of leveling error and leveling error rate, without using gyro output signals explicitly     

Phase-only LMS and perturbation adaptive algorithms

The author defines a phase-only gradient-based adaptive algorithm analogous to the least mean square (LMS) algorithm. Two phase-only perturbation algorithms are then defined. It is shown that the gradient for the perturbation algorithms can be obtained by cross-correlating binary perturbation sequences, that are applied to the adaptive phases, with the resulting instantaneous output power or voltage. It is also shown that a single set of phase-only adaptive weights can be used to simultaneously null interference in multiple output beams. Simulation results are presented for all of the new algorithms. The phase-only perturbation techniques eliminate the need for element level receivers and support low cost retrofitting of adaptive nulling on phased arrays by using conventional beamsteering circuits to apply the adaptive weights     

基于最小区域法的形位公差精确算法

最小区域法是真正符合国际形位公差定义的评价方法。针对现有的最小区域法的实现算法存在思路复杂、对初始计算条件要求较高等问题,提出1种逐次逼近算法。详细阐述了依据此算法求解4种常见形位公差的初始计算条件设置和求解步骤,绘制了以圆度计算为例的算法流程图。结果表明:该算法初始计算条件和思路简单,易于编程,对测点坐标分布及运行环境无特殊要求;可以准确有效地实现圆度等形位公差的评定。     

利用输出误差时间序列模型识别结构时变模态参数

基于振动系统运动方程,建立了描述系统输入和输出关系的输出误差时间序列模型,论证了该模型中外源多项式系数的性质。利用基于UD分解的递归最小二乘参数估计方法估计OE模型的参数,从而得到结构的时变模态参数。数值仿真结果表明用此方法来识别时变模态参数是可行的。     

舰载惯导传递对准仿真系统设计及实现

构建了舰载惯导系统传递对准计算机仿真系统,利用轨迹发生器产生主子惯导系统的惯性器件的输出,送入主子惯导系统的惯导解算模块,产生不同匹配模块的惯导参数输出,进而进行滤波估计。仿真系统可以对所建立的舰载惯导系统的误差模型和滤波算法进行计算机仿真验证,提高误差模型的精确性和滤波算法的可靠性,并对后续的半实物仿真和实际装备实验验证提供理论依据和支撑。     

捷联惯性导航传统比力积分算法与对偶四元数算法的比较研究

捷联惯性导航速度更新算法中,将载体坐标系中的比力积分增量变换到导航参考坐标系中,载体姿态变化的影响通常采用一阶近似模型进行计算。本文分析了包括角振动和加速运动的动态运动下一阶近似模型的局限,在典型角振动及加速运动条件下对忽略姿态变化二阶项造成的速度更新误差进行了理论分析。通过比较,揭示了对偶四元数比力积分算法精度高于传统比力积分算法的原因,即和传统比力积分算法相比,对偶四元数比力积分算法等价于考虑了载体姿态变化影响的二阶项。通过典型角振动及加速运动条件下传统捷联惯性导航算法和对偶四元数导航算法的仿真比较,对理论分析结果进行了验证。     

基于相关误差的变步长LMS算法

以自适应对象建模为应用背景,对不同的变步长LMS算法进行对比研究,提出了一种基于相关误差的变步长LMS算法,仿真结果表明,该算法较现有算法具有较快的收敛速度和较高的收敛精度.     

小波网络在某型航空发动机故障诊断中的应用

分析了用小波网络替代神经网络进行模型辨识的优点,在误差反向传播算法(Error back prop-agation)的基础上,结合小波分析理论,设计了一种小波网络算法.通过对某型航空发动机的仿真实验,小波网络能够及时准确地预测出发动机的输出.同时利用小波变换对残差的分析可以有效地检测出系统所发生的故障.      

考虑输入饱和的两飞轮驱动航天器视线轴控制

 研究了非零初始整星角动量下两飞轮驱动航天器姿态机动控制问题,针对在两飞轮旋转轴平面外某一视线轴的指向控制,给出了目标姿态生成算法和一种抗输入饱和的视线轴指向滑模控制算法。首先,通过对两飞轮驱动航天器静止时的可行姿态进行分析,给出了视线轴指向目标方向的必要条件。然后,基于该条件得到了使视线轴与目标方向偏差最小的目标姿态。最后,对状态方程进行降维和线性化处理,设计了一种抗输入饱和的视线轴指向滑模控制算法。仿真结果展示了本文算法的可行性和有效性。