基于自适应观测器的四旋翼无人飞行器多传感器故障诊断方法

为了实现对四旋翼无人飞行器多传感器故障检测与诊断,提出一种基于自适应观测器的多传感器故障诊断方法。首先,在建立飞行器动力学模型和传感器模型的基础上,将传感器故障视为虚拟执行器故障,构建四旋翼无人飞行器多传感器故障检测与诊断系统;其次,设计非线性观测器实现多故障检测和与隔离,基于Laypunov方法设计非线性自适应观测器实现对多故障偏差值的估计;最后,在传感器测量噪声存在的情况下,证明自适应律的稳定性和参数收敛性。实验结果表明,该方法能有效进行多传感器的故障检测与隔离,实现对多传感器故障偏差的同时估计与跟踪。     

基于改进BP神经网络的传感器集成故障诊断

针对传感器故障，提出了一种基于改进的BP神经网络的集成故障诊断方法。在测量回路中引入“等价偏差”向量，用改进的BP网络建立传感器故障模型，对系统的状态和故障参数进行在线估计，然后将故障参数与修正的Bayes分类算法（MB算法）相结合．进行传感器故障在线检测、分离和估计。对连续搅拌釜式反应器（CSTR）的仿真结果表明，该集成故障诊断方法能够对多重传感器故障进行快速准确的分离和估计，并对传感器故障具有容错性。     

模糊非线性奇偶方程故障诊断方法

研究基于模糊模型和奇偶(一致性)方程的非线性系统执行器故障诊断方法.讨论了全 解耦奇偶方程的产生方法,并给出了全解耦奇偶向量存在的条件.由全解耦奇偶方程产生的残 差仅对特定执行器故障敏感,而与系统状态、扰动输入和其它执行器输入无关.用T-S模糊模型 描述非线性系统,并与全解耦奇偶方程相结合得到了模糊奇偶方程,解决了奇偶方程在非线性 系统中的应用问题.将执行器故障模型用刻度因子和偏差表示,用模糊奇偶方程产生残差,从而 可以估计故障模型的参数.文章给出了某飞机非线性模型的仿真实例.     

一种滑模观测器的多故障诊断方法

针对非线性系统的执行器故障及传感器故障,提出一种鲁棒多故障检测方法．首先,对可能发生的每种执行器故障分别构造模型,并设计相应的滑模观测器用于残差生成,从而实现执行器故障检测．然后,设计一种算法,利用简单滤波器将传感器故障转换为执行器故障,从而直接利用执行器故障检测的方法实现传感器故障的检测,将执行器故障的检测方法推广到执行器、传感器故障同时存在的情况．最后,通过在单关节机械手中的仿真应用验证了所提方法的有效性．     

汽车稳定控制系统执行器与传感器故障诊断与估计

汽车稳定控制系统具有明显的非线性特性,对其传感器与执行器的故障诊断具有一定难度。从汽车八自由度模型入手,简化模型并建立虚拟输入。针对虚拟输入模型,将虚拟执行器故障作为增广状态向量的一部分,构造一个增广系统。利用李雅普诺夫理论设计观测器,获得原系统状态与虚拟执行器故障的渐近估计,并分析了增广观测器的稳定性与收敛性。通过LMI技术实现线性矩阵不等式求解,完成观测器设计。选择一个新的状态变量作为输出信号滤波器,将传感器故障转化为执行器故障,建立二增广系统,直接利用执行器故障诊断方法实现传感器故障诊断。通过Simulink,验证了此方法的可行性。与其他方法相比,该方法不仅能够同时得到虚拟执行器与传感器故障信号,为系统提供警示,而且为后续的容错控制设计提供可靠数据。     

延迟不确定马尔科夫跳变系统的执行器和传感器故障同时估计方法

针对具有参数不确定和延迟环节的马尔科夫跳变系统,在状态转移概率矩阵（Transition probability matrix,TPM）不确定的情形下,讨论了其执行器和传感器故障同时估计的方法.通过扩展系统状态,将系统转换为一个具有马尔科夫跳变参数的广义描述系统,基于此广义描述系统设计马尔科夫跳变观测器实现对其状态和传感器故障的估计.与此同时,还设计了一组自适应律对执行器故障进行在线调节.通过求解一组线性矩阵不等式最优化问题,得到观测器存在的充分条件.最后,针对两个数值实例,验证了所设计方法的有效性.     

不确定线性系统有限时间状态估计与故障重构

针对具有外部干扰和执行器故障的不确定线性系统,给出了一种有限时间内估计系统状态及重构执行器故障的方法．首先,通过状态和输出等价变换,得到不受执行器故障和建模不确定信息干扰的降维解耦系统．在此基础上设计有限时间状态估计器,并设置任意小的时延参数,实现对降维系统状态的有限时间估计,从而达到对原系统状态有限时间估计的目的;其次,考虑高增益滑模微分器对系统输出微分进行有限时间估计;之后,在原系统状态和系统输出微分有限时间估计的基础上,提出一种对系统不确定信息和执行器故障同时估计的方法;最后,通过对具有执行器故障的F-16飞行器纵向系统模型进行仿真,验证所提方法的有效性．     

线性不确定系统具有完整性的一种设计方法

研究了参数不确定状态反馈控制系统的鲁棒容错问题，目的是设计状态反馈控制器使得对可容许的参数不确定性及传感器失效故障或／和执行器失效故障，系统依然保持全局渐近稳定，具有完整性。基于Lyapunov稳定性理论，应用矩阵的谱半径和最小特征值等概念，分别针对传感器失效、执行器失效以及传感器和执行器同时失效三种情况，推导出使闭环系统渐近稳定的三个定理，给出了一种新的、简单的控制律设计方法和设计步骤。最后，给出了一个设计算例，并就各种可能的传感器或／和执行器失效故障情况进行了仿真分析，应用研究结果验证了所提出方法的有效性和简单性。     

基于广义未知输入观测器的鲁棒故障估计方法

当干扰存在时,有效地估计故障且放松故障的限制条件需要进一步的研究,为此针对含未知干扰的非线性连续系统的鲁棒故障估计问题提出一种广义未知输入观测器方法。首先,将执行器故障向量和传感器故障向量与原系统状态向量组成广义系统,放松对故障类型的限制,对此广义系统设计未知输入观测器解耦干扰,保证鲁棒性的同时估计出状态变量、执行器故障及其一阶微分和传感器故障。然后通过解线性矩阵不等式（LMI）给出估计误差渐近收敛的条件。最后,在MATLAB 的simulink平台上用三叶片水平轴风力模型仿真验证本文观测器的故障估计有效性鲁棒性。     

具有积分测量和时延的离散线性变参数系统故障与状态估计

故障检测与诊断(FDD)技术可以有效地提高系统的安全性和可靠性,因此受到越来越多的关注.目前,关于离散系统的状态和故障估计问题的研究还不够充分.本文针对具有积分测量和时延的线性变参数(LPV)系统,提出了一种同时估计执行器/传感器故障和状态的方法.首先,系统当前状态、系统延迟状态和传感器故障构造一个扩维状态,得到广义离散LPV系统.其次,给出了该观测器存在的充分条件并证明观测器是H∞稳定的.然后,将系统状态、执行器和传感器故障的同时估计转化为矩阵不等式的求解问题,给出了观测器待设计矩阵的计算过程.最后,通过仿真验证了该方法的有效性.     

基于卡尔曼滤波器组的多重故障诊断方法研究

针对缺乏有效的用于处理多重(两重及以上)加性故障隔离问题的诊断方法的现状,本文提出了一种新的基于卡尔曼滤波器组的控制系统多重故障的检测与隔离算法.通过构造多个结构不同的卡尔曼滤波器并设计相应的残差,使得每个残差仅对执行机构或传感器某个故障敏感而对其余故障不敏感,最终实现多重故障检测与隔离.除此之外,通过理论推导以及仿真分析,证明了所提出的故障检测与隔离算法的优越性.     

A fault detection and isolation method for complex industrialsystems

A method for the fault detection in complex industrial systems is presented. The plant devices, the sensors, the actuators, and diagnostic tests are described as discrete-event systems. A formal composition rule of these models is given and an inference procedure for the identification and isolation of the faults is discussed. Temporal information is also considered to speed up the fault isolation procedure and, in some cases, to uniquely identify faults with the same static fault signature     

Design of paramet ric fault detection systems: An H- infinity optimization app roach

Problems related to the design of observer- based parametric fault detection (PFD) systems are studied. The core of our study is to first describe the faults occurring in system actuators , sensors and components in the form of additive parameter deviations ,then to transform the PFD problems into a similar additive fault setup , based on which an optimal observer- based optimization fault detection approach is proposed. A constructive solution optimal in the sense of minimizing a certain performance index is developed. The main results consist of defining parametric fault detectability , formulating a PFD optimization problem and its solution. A numerical example to demonstrate the effectiveness of the proposed approach is provided.     

Design of parametric fault detection systems： An H-infinity optimization approach

Problems related to the design of observer-based parametric fault detection (PFD) systems are studied. The core of our study is to first describe the faults occurring in system actuators, sensors and components in the form of additive parameter deviations,then to transform the PFD problems into a similar additive fault setup, based on which an optimal observer-based optimization fault detection approach is proposed. A constructive solution optimal in the sense of mininfizing a certain peffomaance index is developed. The main results consist of defining parametric fauk detectability, formulating a PFD optimization problem and its solution.A numerical example to demonstrate the effectiveness of the proposed approach is provided.     

Robust fault diagnosis for non-linear difference-algebraic systems

A new robust fault diagnosis method based on linear matrix inequality (LMI) for non-linear difference-algebraic systems (DAS) with uncertainties is proposed. Based on the known nominal model of DAS, it firstly constructs an auxiliary system consisting of a difference equation and an algebraic equation, then converts the problem of fault identification into the problem of parameter estimation, and finally realizes fault identification using an LMI method. This method can not only detect, isolate and identify faults for DAS, but also give the upper bounds of fault identification error. Simulation indicates that it can give satisfactory diagnostic results for both abrupt and incipient faults.     

Experimental evaluation of fault diagnosis in a skew-configured UAV sensor system

This paper presents an experimental evaluation of a hybrid fault detection and isolation scheme against three successive faults in skew-configured inertial sensors of an unmanned aerial vehicle (UAV). An additional small and low-cost inertial measurement unit is installed with a skewed angle to a primary inertial measurement unit. A parity space method and an in-lane monitoring method are combined to increase system tolerance to the occurrence of multiple successive faults during flight. The first and second faults are detected and isolated by the parity space method. The third fault is detected by the parity space method and isolated by the in-lane monitoring method based on the discrete wavelet transform. Hardware in-the-loop tests and flight experiments with a fixed-wing UAV are performed to verify the performance of the proposed fault diagnosis scheme.     

Fault detection for multirate sampled-data systems with time delays

In this paper, problems of fault detection in multirate sampled-data (MSD) systems with time delays are studied. The background of our study is the increasing demands for fault detection in complex, distributed process control systems, where the plant, controllers, sensors and actuators are networked by standardized bus systems. The basic idea behand the problem solutions is to derive the parity relations of the MSD system with time delays by taking into account the multirate nature and the multiple time delays. Having introduced two operators, the fault detection problem can be formulated as an optimization problem in the framework of the well-known parity space method. Application and advantages of the approaches proposed in this paper are finally illustrated by simulation examples and by comparison with other indirect design approaches.     

基于LMI的非线性差分-代数系统的鲁棒故障诊断

针对非线性不确定差分-代数系统(DAS),提出了一种基于线性矩阵不等式(LMI)的故 障诊断方法.该方法首先利用已知的DAS标称模型构造一个由差分方程和代数方程组成的辅 助系统,然后基于状态差值与差分方程中的故障以及约束输出差值与代数方程中的故障之间的 关系,将故障辨识问题转化为参数估计问题,最后利用LMI方法对故障进行在线辨识.仿真结 果表明了该方法的有效性.