基于滑模观测器的车辆电子稳定性控制系统故障重构

针对车辆电子稳定性控制系统的横摆角速度传感器和侧向加速度传感器故障检测和重构问题,使用T-S模糊系统建立了车辆动力学系统的全局模型,依据滑模控制理论,给出了基于滑模观测器的传感器故障检测和重构方法,且所设计观测器满足给定的从未知输入到故障重构误差的L2增益性能要求.最后通过实测数据,验证了方法是可行的.     

基于滑模观测器和广义观测器的故障估计方法

针对受未知干扰影响的一类非线性系统,提出一种基于滑模观测器和广义观测器的执行器故障和传感器故障估计方法.首先通过线性变换将原系统解耦为两个降阶的子系统,其中一个子系统受执行器故障和干扰的影响,另一个含有传感器故障和干扰,进一步将后一个子系统转化为广义系统.对两类子系统分别设计滑模观测器和广义观测器,给出估计误差一致最终有界的条件,得到系统状态和未知干扰的估计值.然后,利用等效输出控制原理重构执行器故障,引入干扰补偿保证重构算法的鲁棒性,再根据广义观测器的结果获得传感器故障的估计值.最后,通过计算机仿真验证了本文方法的有效性.     

基于高增益鲁棒滑模观测器的故障检测和隔离

针对一类同时具有执行器和传感器故障的不确定线性系统,讨论了基于观测器的故障检测和隔离方法.首先,通过引入增维向量,使得在构造的增维系统中,故障向量包含了原系统的执行器故障和传感器故障.通过构造辅助输出使增维系统的观测器匹配条件得以满足,同时设计高增益滑模观测器对辅助输出进行估计.然后,对增维系统构造鲁棒滑模观测器并用作故障检测观测器,通过滑模控制项来抑制干扰,使观测器具有鲁棒性.在此基础上,结合多观测器故障隔离思想,提出了可以同时对执行器故障和传感器故障进行检测和隔离的方法. 最后,通过对一个五阶飞行器模型进行仿真,证明了所提方法的有效性.     

基于滑模观测器的机器人系统传感器故障诊断

本文基于变结构滑模状态观测器，提出了一种在模型含有不确定性误差的情况下，机器人控制系统传感器故障的鲁棒诊断方法．通过滑模观测器与传感器的输出构造残差信号，然后从残差中提取故障信息，实现故障的检测与分离．实验结果验证了该方法的有效性．     

基于滑模观测器的电流传感器故障监测系统设计

为了保证电流传感器的工作效率,为设备维护提供有效参考,利用滑模观测器优化设计了电流传感器故障监测系统。加设滑模观测器,改装数据采集器、信号处理器等设备,调整系统的电路连接方式,在抗干扰约束下,完成硬件系统的优化设计。根据电流传感器的工作原理和故障特征,构建电流传感器故障数学模型。利用滑模观测器及其逻辑原理,实时收集电流传感器的运行信号。根据传感器故障特征,选择并计算电流传感器的故障监测指标,通过与设置故障标准的比对,输出可视化的电流传感器故障监测结果。通过系统测试实验得出,基于滑模观测器的电流传感器故障监测系统的指标监测误差、故障类型误检率和漏检率均低于预设值,由此证明优化设计系统具有良好的故障监测功能。     

非线性网络控制系统的故障检测

针对一类具有随机丢包的非线性网络控制系统,研究了系统的故障检测问题.基于T-S模糊模型将对象线性化,考虑了控制器和执行器之间、控制器和传感器之间的随机丢包现象,采用满足Bernoulli分布的二进制序列来描述数据传输的随机丢包.同时利用模糊主导子系统规则,设计了模糊观测器,给出了基于观测器闭环系统渐近稳定的充分条件,并通过数值仿真实验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...     

基于滑模观测器的多传感器故障诊断方法

针对一类非线性系统的传感器故障诊断问题,提出了一种多传感器故障检测方法.首先,定义一个状态变量将传感器故障转换成伪执行器故障,然后设计相应的滑模观测器生成残差,来实现多故障的检测.依据Lyapunov稳定性理论,以LMI的形式给出了观测器存在的充分条件.最后通过单关节机械手的实例,验证了所提方法的有效性和可行性.     

基于级联观测器的多输入多输出非线性系统的故障诊断

将块观测方法应用于非线性系统的故障检测和分离.首先给出了非线性系统的块观测形式,针对传感器故障和执行器故障对非线性系统进行分块,得到了带有故障系统的块观测器形式.利用滑模观测器实现系统状态观测,得到观测器误差;利用所设计的观测器对非线性系统进行故障的诊断和分离;采用等效输出注入概念重构了故障信号,使得多变量输入输出非线性系统的故障诊断问题得到了解耦;针对异步电动机系统实现了传感器故障的分离.仿真结果证明了算法的有效性.     

一种基于全维和降维观测器的故障检测和重构方法

针对不确定性非线性系统,讨论基于观测器的执行器故障的检测和重构方法.首先,通过设计滑模全维观测器,产生对干扰具有鲁棒性、对故障具有敏感性的残差,来达到故障检测的目的;然后,设计能直接消去干扰和故障影响的降维观测器来达到状态渐近收敛估计,并在此基础上,提出故障重构方法;基于微分方程的数值解方法和函数微分的数值解方法,给出一种故障重构的数值解方法;最后,对一个实际模型进行仿真,结果表明了该方法的实用性.     

Robust fault diagnosis scheme in a class of nonlinear system based on UIO and fuzzy residual

In this study, a novel robust fault diagnosis scheme is developed for a class of nonlinear systems when both fault and disturbance are considered. The proposed scheme includes both component and sensor fault with nonlinear system that transferred to nonlinear Takagi-Sugeno (T-S) model. It considers a larger category of nonlinear system when fuzzification is used for only nonlinear distribution matrices. In fact the proposed method covers nonlinear systems could not transform to linear T-S model. This paper studies the problem of robust fault diagnosis based on two fuzzy nonlinear observers, the first one is a fuzzy nonlinear unknown input observer (FNUIO) and the other is a fuzzy nonlinear Luenberger observer (FNLO). This approach decouples the faulty subsystem from the rest of the system through a series of transformations. Then, the objective is to design FNUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method; meanwhile, FNLO is designed for faulty subsystem to generate fuzzy residual signal based on a quadratic Lyapunov function and some matrices inequality convexification techniques. FNUIO affects only the fault free subsystem and completely removes any unknown inputs such as disturbances when residual signal is generated by FNLO is affected by component or sensor fault. This novelty and using nonlinear system in T-S model make the proposed method extremely effective from last decade literature. Sufficient conditions are established in order to guarantee the convergence of the state estimation error. Thus, a residual generator is determined on the basis of LMI conditions such that the estimation error is completely sensitive to fault vector and insensitive to the unknown inputs. Finally, an numerical example is given to show the highly effectiveness of the proposed fault diagnosis scheme.     

Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection

In this paper, the extension of the Bayesian framework for sensor fault detection of nonlinear systems proposed in [25] is studied utilizing particle filtering and the expectation maximization (EM) algorithm, in which the fault probability is calculated. The proposed algorithm is implemented on a wind turbine benchmark model to detect drivetrain sensor faults, which are one of the most addressed and likely faults in offshore wind turbines. The fault probability estimation effectively eliminates the need for installing identical redundant sensors. Indeed, because of the use of the unknown wind speed estimator, the residual signal, constructed based on the drivetrain estimated states, is not able to clearly signify the fault periods, a situation in which the fault probability accurately does this task. Also, using the proposed algorithm, the fault size for each sensor is estimated via a one‐step calculation, which decreases the complexity of this algorithm. The fault identification is performed using the recursive least square method and two other modifications, including exponentially weighted and windowed estimates. Additionally, in the fault accommodation step, the concept of a virtual sensor is used to remove the need for reconfiguring the current controller, which reduces complexity and expense. In the simulation section, using a real measured wind speed for two different fault scenarios, the proposed algorithm is evaluated and finally, conclusions are stated.     

A geometric approach to nonlinear fault detection and isolation in a hybrid three-cell converter

In this paper, the problem of fault detection and isolation in a three-cell converter is investigated using a nonlinear geometric approach. This powerful method based on the unobservability distribution is used to detect and isolate the faulty cell in the three-cell converter. First, a model describing the faults in the cells is presented. The geometric approach is then applied on this faulty model to generate residual signals based on a sliding-mode observer that allows the detection of faults in the three-cell converter. Numerical results show the effectiveness of the proposed sliding-mode residual generators for fault detection and isolation in the three-cell converter.     

基于自适应观测器的四旋翼无人飞行器传感器故障诊断方法

针对四旋翼无人飞行器传感器故障诊断问题,提出一种用于四旋翼无人飞行器加速度计和陀螺仪故障同时发生的故障检测与隔离以及故障偏差值估计的非线性诊断方法.首先,在建立飞行器动力学模型和传感器模型的基础上,构建四旋翼无人飞行器传感器故障检测与诊断系统.其次,利用故障观测器完成传感器故障的检测与隔离,基于Laypunov方法设计非线性自适应观测器对未知故障偏差值进行估计.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,该方法能有效进行传感器的故障检测与隔离,实现对传感器故障偏差的估计与跟踪.     

Integrated fault estimation and fault‐tolerant control for stochastic systems with Brownian motions

This paper presents an integrated robust fault estimation and fault‐tolerant control technique for stochastic systems subjected to Brownian parameter perturbations. The augmented system approach, unknown input observer method, and optimization technique are integrated to achieve robust simultaneous estimates of the system states and the means of faults concerned. Meanwhile, a robust fault‐tolerant control strategy is developed by using actuator and sensor signal compensation techniques. Stochastic linear time‐invariant systems, stochastic systems with Lipschitz nonlinear constraint, and stochastic systems with quadratic inner‐bounded nonlinear constraint are respectively investigated, and the corresponding fault‐tolerant control algorithms are addressed. Finally, the effectiveness of the proposed fault‐tolerant control techniques is demonstrated via the drivetrain system of a 4.8 MW benchmark wind turbine, a 3‐tank system, and a numerical nonlinear model.     

变速风力发电机故障观测器的设计和应用

针对模型包含未知非线性函数的变速风力发电机故障诊断问题,采用反卷法和最小二乘支持向量机对未知非线性函数进行辨识,将辨识结果作为补偿项加入比例高阶积分观测器中,消除未知非线性项对故障诊断精度的影响,建立了基于最小二乘支持向量机非线性项辩识模型的比例高阶积分故障观测器。李雅普诺夫函数证明该故障观测器的稳定性,仿真实验表明该故障观测器可以准确、快速、有效地诊断变速风力发电机故障。     

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。     

风能转换系统执行器故障主动容错控制

提出了一种新颖的风能转换系统滑模主动容错控制策略.针对风能转换系统执行器故障,运用预测控制思想和迭代算法,设计了一种故障观测器.在设定的优化时域长度内,利用实际系统与故障观测器的输出差值,通过反复迭代运算,不断地对虚拟故障信号进行调整,使其能有效地拟合实际系统执行器故障,并根据故障观测值实时调整滑模容错控制器结构.未发生故障时,故障观测值为零,系统在滑模控制器控制下稳定运行;执行器发生故障时,运用故障观测值实时调整滑模容错控制项,并用双曲正切函数代替符号函数,消除抖动.仿真实验结果表明,滑模容错控制器下的系统具有良好的容错能力,提高了风能转换系统最大风能捕获效率.