Kalman filter based fault diagnosis of networked control system with white noise

The networked control system NCS is regarded as a sampled control system with output time-variant delay. White noise is considered in the model construction of NCS. By using the Kalman filter theory to compute the filter parameters, a Kalman filter is constructed for this NCS.By comparing the output of the filter and the practical system,a residual is generated to diagnose the sensor faults and the actuator faults. Finally, an example is given to show the feasibility of the approach.     

基于Kalman滤波的网络丢包补偿器

针对网络控制系统中普遍存在的连续丢包问题,建立了网络控制系统状态反馈丢包补偿模型,提出了1种基于Kalman滤波的连续丢包补偿器算法。把具有数据包连续丢失补偿的网络控制系统描述为具有3个事件的异步动态系统,并且给出了系统稳定性的证明。该补偿器具有较高的预测补偿和抗干扰能力。仿真结果表明卡尔曼滤波连续丢包补偿器算法的有效性。     

基于无记忆降阶观测器的网络化控制系统故障检测方法

网络化控制系统(Networked Control System,简称NCS)研究的是如何通过网络实现闭 环控制,是涉及控制科学和计算机网络等多个学科知识的跨学科研究领域.本文将网络化控制系 统视为带有时延的采样控制系统,构造了一个无记忆的降阶状态观测器,通过比较观测器输出和 系统实际输出来产生残差,从而对网络化系统的控制故障进行了检测.仿真结果表明此方法是可 行的.     

自适应Kalman滤波器及其应用

对于带未知噪声统计的单输出系统,本文提出了一种新的自适应Kalman滤波器.应用 现代时间序列分析方法,基于ARMA新息模型的滑动平均(MA)参数的在线辨识,提出了 稳态最优Kalman滤波器增益估计的一种新算法,比Mehra的算法简单.同时还提出了辨 识滑动平均(MA)模型参数的一种新的自适应Kalman滤波算法.此外,给出了在雷达跟 踪系统中的应用,且仿真结果说明了本文算法的有效性.     

无人机控制系统传感器故障诊断的方案与仿真

应用卡尔曼滤波器对传感器进行故障诊断时,由于输入噪声和测量噪声的统计特性是不确定的,因此难以得到其准确的统计特性先验信息,而采用错误的噪声统计特性会产生滤波误差,甚至使滤波发散,因此该文提出了一种基于Sage-Husa时变噪声统计估计器的自适应卡尔曼滤波器算法,在滤波过程中利用噪声统计估计器对未知的统计特性进行在线估计,并对无人机控制系统的传感器故障进行在线诊断,此方法无须增加硬件余度和其他解析余度,易于实现,可靠性好,检测迅速.仿真表明该方法能够检测出系统故障并进行故障定位.     

Active fault tolerant control using state-space self-tuning control approach

This paper presents a new fault tolerant control scheme for unknown multivariable stochastic systems by modifying the conventional state-space self-tuning control approach. For the detection of faults, a quantitative criterion is developed by comparing the innovation process errors occurring in the Kalman filter estimation algorithm, which, for faulty system recovery, a weighting matrix resetting technique is developed by adjusting and resetting the covariance matrices of the parameter estimate obtained in the Kalman filter estimation algorithm to improve the parameter estimation of the faulty systems. The proposed method can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection. The modified state-space self-tuning control scheme can be applied to the multivariable stochastic faulty system without requiring prior knowledge of system parameters and noise properties.     

Cascade filter structure for sensor/actuator fault detection and isolation of satellite attitude control system

This paper presents a new scheme for fault detection and isolation in a satellite system. The purpose of this paper is to develop detection, isolation and identification algorithms based on a cascade filter for both total and partial faults in a satellite attitude control system (ACS). The cascade filter consists of a decentralized Kalman filter (DKF) and a bank of interacting multiple model (IMM) filters. The cascade filter is utilized for detection and diagnosis of anticipated sensor and actuator faults in a satellite ACS. Other fault detection and isolation (FDI) schemes are compared with the proposed FDI scheme. The FDI procedure using a cascade filter was developed in three stages. In the first stage, two local filters and a master filter detect sensor faults. In the second stage, the FDI scheme checks sensor residuals to isolate sensor faults, and 11 Extended Kalman filters with actuator fault models detect wherever actuator faults occur. In the third stage of the FDI scheme, four filters identify the fault type, which is either a total or partial fault. An important feature of the proposed FDI scheme is that it can decrease fault isolation time and accomplish not only fault detection and isolation but also fault type identification using a scalar penalty in the conditional density function.     

FAULT DIAGNOSIS OF HYDRAULIC SERVO SYSTEM USING THE UNSCENTED KALMAN FILTER

Fault occurrence can be embodied by the physical parameter variations of the hydraulic servo system. Faults can, therefore, be diagnosed according to the model coefficient variations of the hydraulic servo system. This paper proposes an approach for fault diagnosis based on the unscented Kalman filter (UKF) with a mathematical model of the hydraulic servo system. The mathematical model is established using the dynamic equations of the hydraulic servo system. Based on the fault mechanism analysis results, several important system model parameters that can separately represent different faults in different components of the hydraulic servo system are chosen. Discrete state space equations are derived from the dynamic equations. The UKF algorithm is used to estimate the important system model parameters of the hydraulic servo system by utilizing the discretized state space model. According to the variations of these model parameters, the fault modes and locations of the hydraulic servo system can be diagnosed and isolated. Two types of faults, namely, abrupt fault in servovalve gain and slow wear fault in hydraulic cylinder piston, which cannot be directly detected from the system output, are introduced individually to the hydraulic servo system in this work. By comparing with the extended Kalman Filter, three different experimental cases are used to validate the effectiveness of the UKF for hydraulic servo system fault diagnosis.     

基于降阶卡尔曼滤波器的水下机器人滑模容错控制

针对水下机器人执行器时变、非线性故障,提出一种基于降阶卡尔曼滤波器的故障估计和滑模容错控制方法.用降阶卡尔曼滤波器估计水下机器人故障解耦子系统的状态,受故障的影响,子系统状态可测.由估计的状态和测量的状态可进一步得到水下机器人执行器的故障信息.滑模容错控制器根据所估计的执行器故障调整控制器的输出以实现容错控制.仿真结果验证了所提出的故障辨识与容错控制算法的有效性.     

带滤波的无线传感器网络的H∞控制

本文研究了基于滤波的无线传感器网络的H∞控制问题.首先建立了无线传感器网络的模型,然后使用稳态Kalman滤波器对多个传感器节点的输出信号进行滤波,将滤波后的信号作为无线传感器网络系统的反馈输出,并通过建模和分析得到带干扰的线性离散切换系统模型.最后设计了切换控制器,并基于Lyapunov函数给出了在任意切换条件下使该切换系统满足H∞控制的条件.仿真结果验证了该方法和结果的可行性.     

基于LMS算法的水下航行器传感器故障检测

为了对水下航行器控制系统的传感器故障进行在线检测,采用有限脉冲响应(FIR)滤波器对水下航行器进行在线自适应建模,利用LMS算法来调节自适应滤波器的权系数,通过对FIR滤波器权系数的分析,实时检测出系统的传感器故障.通过Matlab仿真表明,该方法简单、可靠,可对水下航行器控制系统的传感器故障进行实时检测.     

自适应卡尔曼滤波在组合导航中的应用研究

对惯性导航系统（INS）与全球导航系统（GPS）分别进行了具体探讨,对比了两者的优缺点,针对INS／GPS组合导航系统中由于模型不准或因量测噪声的复杂多变造成的发散问题,引入了一种基于输出相关法的自适应卡尔曼滤波技术。通过在自适应滤波算法中推算最优稳态增益来调整量测噪声,抑制滤波器的发散,为GPS／INS组合导航系统实现高精度导航提供了有效的途径。仿真结果表明该算法能很好地对系统状态进行最优估计并适应系统噪声的变化,具有比常规卡尔曼滤波更高的导航精度。     

Fault-tolerant control of variable speed limits for freeway work zone using likelihood estimation

Freeway work zone with lane closure can lead to disruption to local traffic and cause significant impacts on mobility, safety and environmental sustainability. To mitigate traffic congestion near work zone area, many variable speed limits (VSL) control approaches have been developed. However, VSL control system, as a critical transportation management system, is prone to the occurrence of traffic sensor faults. Faulty sensors can cause great deviations of traffic measurements and system degradation. Therefore, this study aims to develop a fault-tolerant VSL control strategy for freeway work zone with the consideration of the mainline sensor fault and ramp sensor fault. To analyze the traffic dynamics near work zone area, a traffic flow model has been built first. Then a sliding mode controller in the previous study has been utilized for VSL control. In addition to the traffic states estimated by a Kalman filter, two observers have been developed to provide analytical redundancy of traffic states estimation. By comparing the logarithm of the likelihood estimations from the Kalman filter and two observers, a fault diagnosis scheme has been designed to detect and identify the faults of mainline sensors and ramp sensors. Then the VSL controller can be reconfigured accordingly in case of sensor faults. The proposed system is implemented and evaluated under a realistic freeway work zone environment using traffic simulator SUMO. The results demonstrate that the developed system can accurately detect and identify the sensor faults in real time. Consistent improvements of mobility, safety and sustainability are also achieved under fault-free and sensor faults scenarios.     

Robust unscented Kalman filter for nanosat attitude estimation

A robust unscented Kalman filter based on a multiplicative quaternion-error approach is proposed for nanosat estimation in the presence of measurement faults. The global attitude parameterization is given by a quaternion, while the local attitude error is defined using a generalized three-dimensional attitude representation. The proposed algorithm uses a statistical function including measurement residuals to detect measurement faults and then uses an adaptation scheme based on multiple measurement scale factor for filter robustness against faulty measurements. The proposed algorithm is demonstrated for the attitude estimation of a nanosat with an on-board three-axis magnetometer and rate-integrating gyros in the presence of measurement faults as well as satellite orbit errors. To compare the estimation performance of the proposed algorithm, the robust unscented Kalman filter with single measurement noise scale factor, the standard extended Kalman filter and the unscented Kalman filter are also implemented under the same simulation conditions.     

Kalman filter-based multialternative method for fault detection and estimation

Proposed was a computationally efficient multialternative method for detection and estimation of faults additively involved in the right-hand sides of the linear equations of the state and measurement vectors. In distinction to the classical approach using a Kalman filter bank for each fault, the paper suggested an extended Kalman filter estimating a set of possible faults. The a posteriori probabilities and estimates of individual faults were shown to be readily calculable from the estimates and covariance matrices generated by the extended Kalman filter. Efficiency of the method is corroborated by modeling of a navigation complex with two inertial systems.     

基于改进自适应滤波算法的控制系统传感器故障诊断

应用标准的多模自适应滤波算法进行故障诊断时,需要一组与假设模型等数量的卡尔曼滤波器进行滤波计算,计算量很大而且耗时,这对于实时性要求很强的工程应用是不合适的,因此提出了一种改进自适应滤波算法,它只需要单个卡尔曼滤波器就可得到与标准的多模自适应滤波算法等价的残差,因此可以有效地减少计算量和计算时间.将此算法应用于某无人机控制系统的传感器故障诊断,仿真结果验证了该方法的有效性.     

基于神经网络观测器的飞行器传感器故障检测

为了避免传感器故障对飞控系统的影响,实现传感器故障的快速检测与隔离,提出了一种基于神经网络观测器(NNOB)的传感器故障检测方法.在建立四旋翼飞行器姿态故障模型的基础上,利用非线性观测器得到的期望输出和传感器测量值设计基于神经网络(NN)的传感器故障观测器,利用扩展卡尔曼滤波器(EKF)更新神经网络的权值参数,通过Ly...     

FHOSM Tolerant Control for Networked Control Systems with Disturbances and Faults

This paper investigates the networked control system (NCS) with faults and disturbances which affect both actuator and sensor. A fast high-order sliding mode (FHOSM) controller is proposed to compensate for actuator faults and sensor disturbances, which is constructed based on the estimated information of the NCS. Accordingly, an adaptive observer with multi-stage is designed to estimate the states and sensor disturbances and protect the NCS from actuator faults. Furthermore, a new sliding function is assembled to realize the finite-time convergence of system states. The stability of the system with the suggested procedure is illustrated by the stability analysis underneath the designed control law. Finally, the simulation results are implemented and confirm the effectiveness of the proposed method in defending the system against both issues and inhibiting system failures.

     

多故障的奇偶方程-参数估计诊断方法

提出一种将奇偶方程与参数估计相结合的多故障诊断方法。构造了一种新的奇俩方程，其产生的残差仅对一个传感器故障和一个执行器故障敏感。将传感器和执行器故障模型表示成刘度因子和偏差的形式，应用卡尔曼滤没方法对各故障模型参数进行估计。某型号飞机控制系统的仿真结果表明，新方法能对传感器故障和执行器故障同时存在的线性系统进行诊断，有效地估计出各故障的模型参数。     

基于输出残差的自适应状态估计方法

本文针对线性定常离散系统提出了一种有效的基于输出残差的自适应状态估计方法，首先利用输出残差给出了卡尔曼滤波增益马尔可夫参数估计，并且得到了卡尔曼滤波增益估计，然后解决了自适应状态估计问题，最后以仿真实例说明了此方法的有效性。