确定采样型强跟踪滤波飞机舵面故障诊断与隔离

为了克服扩展多模型自适应估计中扩展卡尔曼滤波的理论局限性,多重渐消因子强跟踪改进引起的滤波发散现象以及多维高斯故障概率计算量大等问题,本文将一类基于确定解析采样近似方法的非线性次优高斯滤波与多模型自适应估计相结合,提出了改进的多重渐消因子强跟踪非线性滤波快速故障诊断方法.确定采样型滤波克服了扩展卡尔曼滤波的理论局限性;推导了等效多重渐消因子计算方法,避免了非线性系统雅克比矩阵的计算,提高了故障突变时的跟踪性能;提出了基于平方根分解的改进的一步预测协方差更新方程,保证了滤波稳定性;提出了基于欧几里得范数简化的故障概率计算方法,降低了计算量.通过对比仿真验证了3种不同非线性滤波算法及其强跟踪改进算法的有效性,故障诊断方法跟踪性强、速度快、精度高,具有较好的鲁棒性和稳定性.     

基于自适应固定滞后卡尔曼平滑器的状态观测器在假手上的应用

针对以电位计为角度传感器的假手系统,提出了一种基于自适应固定滞后卡尔曼平滑器的状态观测器以观测手指的当前位置、速度和加速度信息.首先,分析了卡尔曼滤波器滤除电位计热噪声并观测速度与加速度的合理性,进而建立了其系统的离散状态转移矩阵.其次,相比卡尔曼滤波器,卡尔曼平滑器在参数相同的情况下具有更好的平滑效果,据此提出一种基于固定滞后卡尔曼平滑器的状态观测器,并通过引入渐消因子以提高动态响应特性.同时给出了一种将本文算法滞后特性降至一个控制周期的有效实现方式.最后,在HIT-V仿人假手实验平台上进行了实验验证.实验结果表明,相比对原始数据直接进行差分,该方法将速度噪声降低了20倍以上,加速度噪声降低了10 000倍以上.相比标准卡尔曼滤波器和固定滞后卡尔曼平滑器,该方法在动态响应方面具有更好的效果.     

基于AFKF的多旋翼无人机组合导航技术研究

目前旋翼无人机组合导航系统大都使用扩展卡尔曼滤波算法,然而由于导航系统建模误差和传感器测量精度的影响,导航信息解算误差较大。为了改善旋翼无人机的飞行控制效果,应用自适应渐消卡尔曼滤波（Adaptive fading Kalman filter,AFKF）进行旋翼无人机组合导航解算,算法通过实时计算遗忘因子,对过去的数据权重进行削减,以提高扩展卡尔曼滤波算法的自适应能力。应用旋翼无人机真实飞行数据进行仿真,仿真结果表明,自适应渐消卡尔曼滤波算法能够有效抑制建模误差,弥补传感器测量精度不足,改善旋翼无人机组合导航解算结果。     

Unknown Input Observer-based Actuator and Sensor Fault Estimation Technique for Uncertain Discrete Time Takagi-Sugeno Systems

This paper presents an Unknown Input robust Observer (UIO) capable of simultaneously estimate both sensor fault and system states. The system is assumed to be discrete-time Takagi-Sugeno (T-S) Fuzzy with uncertainties. An augmented system is obtained from the dynamic fault model and original system. Afterward, a UIO is designed for the augmented system aiming at decoupling process disturbances. Its design is obtained by using an H_∞ optimization technique and developed to maintain the observer stable, reducing the non-decoupled process disturbances effect. The proposed method is validated by two numerical examples as it is compared to a regular UIO technique and the extended Kalman filter. Results show the proposed technique presents better performance when the dynamic system is not purely nonlinear even if the same tuning parameters are chosen. Although other techniques are not able to ensure the error limitation, the proposed one is capable of it even in nonlinear systems.

     

基于IGGIII方案的自适应渐消卡尔曼滤波器

渐消卡尔曼滤波器滑动采样区间长度[N]的取值为单一定值,难以实现滤波精度与动态性之间的平衡。针对该问题,提出了一种基于IGGIII方案的自适应渐消卡尔曼滤波器。该滤波器进行新息异常卡方检测,通过构建一个类似于IGGIII权函数的三段式滑动采样区间长度[N]的取值函数,实现滑动采样区间长度[N]的自适应优化选取,提升滤波精度。Matlab仿真结果证明,基于IGGIII方案的自适应渐消卡尔曼滤波器在系统稳定情况下滤波结果更加精确,系统模型参数发生变化时具有更好的动态性,能够实现滤波精度与动态性之间的平衡。     

Deep auto-encoder observer multiple-model fast aircraft actuator fault diagnosis algorithm

In the extended multiple model adaptive estimation fault diagnosis algorithm, the extended Kalman filter has theoretical limitations, and the establishment of accurate aircraft mathematical model is almost impossible. Meanwhile, there is no automatic method to optimally select the node number of deep neural network hidden layer. In this paper, a deep auto-encoder observer multiple-model fault diagnosis algorithm for aircraft actuator fault is proposed. Based on the empirical formula of the basic auto-encoder hidden layer node number selection (three layered neural network), the recursive formula for deep auto-encoder hidden layer node number selection are proposed. The deep auto-encoder observers for no-fault and different actuator faults are trained to observe the system state. Combined with multiple model adaptive estimation, the deep auto-encoder observer overcomes the theoretical limitation of extended Kalman filter, and avoided the calculation of the nonlinear system Jacobian matrix. The simulation results show that hidden layer node number selection recursive formula is useful. The fault diagnosis algorithm is more efficient and has better performance compared to the standard methods.     

A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

This paper explores multiple model adaptive estimation (MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter-multiple model adaptive estimation unscented Kalman filter (MMAE-UKF) rather than conventional Kalman filter methods, like the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters, which the improved filtering method can overcome. Meanwhile, this algorithm is used for integrated navigation system of strapdown inertial navigation system (SINS) and celestial navigation system (CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.      

基于扩展卡尔曼滤波器的RBF神经网络学习算法

径向基函数（RBF）神经网络可广泛应用于解决信号处理与模式识别问题,目前存在一些学习算法用来确定RBF中心节点和训练网络,对于确定RBF中心节点向量值和网络权重值可以看作同一系统问题,因此该文提出把扩展卡尔曼滤波器（EKF）用于多输入多输出的径向基函数（RBF）神经网络作为其学习算法,当确定神经网络中网络节点的个数后,EKF可以同时确定中心节点向量值和网络权重矩阵,为提高收敛速度提出带有次优渐消因子的扩展卡尔曼滤波器（SFEKF）用于RBF神经网络学习算法,仿真结果说明了在学习过程中应用EKF比常规RBF神经网络有更好的效果,学习速度比梯度下降法明显加快,减少了计算负担。     

强脉冲噪声环境下的多径衰信道估计

提出一种在强干扰脉冲噪声存在下对无线多径信道进行估计的算法.在无线通信系统中,衰落信道可以采用自回归(AR)模型建模,通过RLS算法和自适应Kalman滤波器分别对AR模型的参数进行估计,但是,这两种算法对噪声干扰非常敏感.为了加快RLS算法的收敛性,并有效抑制大脉冲干扰的影响,在算法的改进中引入了抑制因子,用于对脉冲干扰幅度的抑制.仿真结果显示:相比于传统的算法,改进后的算法在联合估计信道时,提高了抵抗大脉冲干扰的能力,加快了待估参数的收敛速度.     

一种提高车辆导航系统定位精度的方法

为了解决量测方程线性化及普通卡尔曼滤波数值稳定性对车载航位推算系统滤波结果的影响,给出了车载航位推算系统的基于U D分解的自适应迭代滤波算法,并将这一算法与车载航位推算系统的迭代型自适应推广卡尔曼滤波算法及简单航位推算进行了比较。计算机仿真结果表明:新算法能够有效地提高车载航位推算系统的定位精度及数值稳定性。     

Adaptive extended Kalman filtering for visual motion estimation of 3D objects

An algorithm for the real-time estimation of the position and orientation of a moving object of known geometry is presented in this paper. An estimation algorithm is adopted where a discrete-time extended Kalman filter computes the object pose on the basis of visual measurements of the object features. The scheme takes advantage of the prediction capability of the extended Kalman filter for the pre-selection of the features to be extracted from the image at each sample time. To enhance the robustness of the algorithm with respect to measurement noise and modelling error, an adaptive version of the extended Kalman filter, customized for visual applications, is proposed. Experimental results on a fixed single-camera visual system are presented to test the performance and the feasibility of the proposed approach.     

The stability analysis of the adaptive fading extended Kalman filter using the innovation covariance

The well-known conventional Kalman filter gives the optimal solution but to do so, it requires an accurate system model and exact stochastic information. However, in a number of practical situations, the system model and the stochastic information are incomplete. The Kalman filter with incomplete information may be degraded or even diverged. To solve this problem, a new adaptive fading filter using a forgetting factor has recently been proposed by Kim and co-authors. This paper analyzes the stability of the adaptive fading extended Kalman filter (AFEKF), which is a nonlinear filter form of the adaptive fading filter. The stability analysis of the AFEKF is based on the analysis result of Reif and co-authors for the EKF. From the analysis results, this paper shows the upper bounded condition of the error covariance for the filter stability and the bounded value of the estimation error. Keywords: Adaptive Kalman filter, forgetting factor, nonlinear filter, stability analysis. Recommended by Editorial Board member Huanshui Zhang under the direction of Editor Young Il Lee. Kwang-Hoon Kim received the Ph.D. degree in the School of Electrical Engineering and Computer Science at Seoul National University in 2006. His research interests include Kalman Filtering, GNSS/INS integration system, and GNSS signal processing algorithm. Gyu-In Jee received the Ph.D. degree in Systems Engineering from Case Western Reserve University in 1989. His research interests include Indoor GPS positioning, Software GPS receiver, GPS/Galileo baseband FPGA design, and IEEE 802.16e based wireless location system. Chan-Gook Park received the Ph.D. degree in Control and Instrumenta-tion Engineering from Seoul National University in 1993. His research interests include INS/GPS integration system, inertial sensor calibration, navigation and control for micro aerial vehicles, and estimation theory. Jang-Gyu Lee received the Ph.D. degree from the University of Pittsburgh in 1977. He is currently a Professor at the School of Electrical Engineering and Computer Science at Seoul National University. His research interests include micro inertial sensors, inertial navigation systems, GPS, and filtering theory.     

衰减因子自适应估计卡尔曼滤波比较研究

针对卡尔曼滤波算法发散的问题,从卡尔曼滤波技术的稳定性出发,分析了卡尔曼滤波发散的原因,提出了新的衰减记忆卡尔曼滤波中衰减因子的自适应估计方法。该方法利用滤波残差序列在最优估计时为零均值白噪声的性质,分别检验滤波残差每一个分量得出衰减因子值,并与强跟踪滤波器进行了对比研究。仿真结果表明,新算法在系统噪声特性不准确的情况下,能自适应地估计出衰减因子的大小,抑制卡尔曼滤波估计的发散,滤波精度要高于强跟踪滤波器;且其推导形式简单、计算量小、适合于在线运算。     

Fault prediction of the nonlinear systems with uncertainty

Fault prediction which can forecast the fault in advance to avoid large calamity has attracted more and more attention. However, the current filter based fault prediction methods for the nonlinear systems are all based on the framework of the probability theory, and cannot realize fault prediction of the nonlinear systems with fuzzy uncertainty. Based on the extended fuzzy Kalman filter (EFKF) and the extended orthogonality principle, an improved fuzzy Kalman filter (IFKF) is firstly proposed to estimate the system states or the parameters in this paper. Then, according to the IFKF, a multi-step improved fuzzy Kalman predictor (MIFKP), which can be considered as an adaptive predictor, is obtained. Once the characteristic parameter is chosen, the MIFKP can be used to implement the multi-step fault prediction. Simulation results demonstrate that the proposed approach has the better prediction ability and stronger robustness than the traditional multi-step extended fuzzy Kalman predictor (MEFKP).     

自适应扩展卡尔曼滤波器在移动机器人定位中的应用

针对移动机器人定位过程中存在的误差积累问题,提出了采用自适应扩展卡尔曼滤波算法(AEKF).分析了扩展卡尔曼滤波(EKF)和AEKF两种算法, AEKF取采样时刻的各项泰勒级数,并利用Sage-Husa时变噪声估计器实时估计观测噪声,克服了线性化误差,增强了环境适应性;同时,对AEKF的收敛性及运算复杂度进行分析,并结合算法实验表明AEKF具有良好的速度精度综合性价比;最后对比分析两种算法实现机器人定位的效果并实验完成误差对比.结果表明AEKF具有更优的定位性能.     

一种鲁棒自适应推广Kalman滤波及其在飞行状态估计中的应用

本文在自适应推广Kalman滤波基础上，为了防止滤波发散，改善自适应Kalman滤波的数值稳定性和计算效率，利用U－D分解滤波，并引进滤波发散的判据等，提出一种鲁棒自适应推广Kalman滤波新算法，并把该算法应用于飞行器飞行状态估计问题，仿真及实际计算结果证明了本文方法的有效性。     

无速度传感器的表面式永磁同步电机无源控制策略

针对高性能的表面式永磁同步电机(SPMSM)调速系统,考虑电机实际运行过程中逆变器非线性因素对传动系统的影响,推导出考虑逆变器的SPMSM系统统一端口受控耗散哈密顿数学模型。基于能量成形方法和端口受控耗散哈密顿系统原理,设计SPMSM驱动系统的无源控制器,利用带扩张状态观测器的自适应滑模控制设计速度调节器,得到??轴期望的电流并实现转速的估计,逆变器非线性扰动由扩张状态观测器进行补偿。仿真结果表明,所提出方法实现了较高的速度估计精度,使调速系统具有优良的动、静态性能。     

基于视觉/惯导的无人机组合导航算法研究

目前视觉惯性组合导航系统多采用优化紧/松耦合以及滤波紧/松耦合算法,应用误差状态卡尔曼滤波能够将较低频率的视觉位姿信息提升到与惯性信息同步的频率;提出一种基于自适应卡尔曼滤波的视觉惯导组合导航算法,首先考虑到系统建模与传感器测量误差,采用自适应渐消卡尔曼滤波进行导航解算,通过实时计算遗忘因子,以调节历史数据的权重,可抑制建模误差,提高组合导航系统性能,然后针对视觉SLAM解算过程造成的视觉位姿信息滞后于惯导信息的问题,提出一种延时补偿方法;仿真实验表明,采用延时补偿的自适应渐消卡尔曼滤波算法能够有效抑制建模误差,并降低视觉位姿信息滞后带来的影响,提高无人机组合导航的解算精度,姿态、速度、位置解算精度分别达到5°、0.5m/s、0.4m以内。     

一种带多重次优渐消因子的扩展卡尔曼滤波器

本文提出了"强跟踪滤波器"的新概念,给出了强跟踪滤波器的一般结构,并提出了一个正交性原理用于此类滤波器的设计.在此基础上,提出了一种具有强跟踪滤波器性能的带多重次优渐消因子的扩展卡尔曼滤波器(SMFEKF),改进了文献[1]中提出的一种带单重次优渐消因子的扩展卡尔曼滤波器(SFEKF).数值仿真说明了SMFEKF的有效性.     

扩展卡尔曼滤波结合前馈补偿永磁同步电机位置估计

转速和转子位置的精确估计对建立永磁同步电机(permanent magnet synchronous moter,PMSM)转速、电流双闭环矢量控制系统非常重要.本文主要研究扩展卡尔曼滤波算法(extended Kalman filter,EKF)估计转速、转子位置问题.与传统EKF估计转子位置方法不同的是,本文采用遗传算法(GA)优化EKF的协方差矩阵,并给出P,Q,R矩阵选取过程.另外将负载转矩观测器观测的负载转矩同速度调节器的输出一起作为电流调节器的控制变量.仿真及实验结果表明:文中提出的新方法有效缩短系统协方差参数选取时间,提高转速的辨识精度和抗负载扰动能力.