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.     

基于滑模观测器的永磁同步电机无位置传感器控制的研究

简要论述滑模观测器的理论基础,根据PMSM的数学模型,建立基于滑模观测器的PMSM无传感器控制的系统模型。根据滑模观测器原理,通过电机的定子电压和相电流估算出电机的转角和转速。利用MATU姬工具建立无位置传感器的永磁同步电动机调速系统的仿真平台,仿真实验检验滑模观测器法的有效性。在采用DSP2812的伺服控制平台上,验证滑模观测器法的正确性和可行性。实验结果表明滑模观测器法具有良好的动静态性能。     

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．     

风力发电系统传感器故障诊断

针对非线性风力发电系统,提出了一种基于滑模观测器的传感器故障诊断方法.基于考虑传感器加性故障的非线性动态模型,利用T--S模糊理论建立风力发电系统全局T--S模型,设计模糊T--S系统滑模故障观测器,产生对故障具有敏感性的残差,实现故障检测.通过等价输出控制方法来维持滑模运动,直接获取故障信息,重构传感器故障.最后以三叶片水平轴风力发电系统为例,仿真验证了该方法的有效性与可靠性.     

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.     

基于多项式混沌理论的感应电机自愈控制系统

针对感应电机控制系统中速度传感器故障提出了一种自愈控制方法.考虑转子电阻不确定性,基于多项式混沌理论(PCT),对定子电流模型进行扩展,再利用扩展Kalman滤波法,设计定子电流观测器,并在此基础上设计速度自适应估计器.当传感器正常时,利用电流观测器观测值进行状态监测;传感器发生故障后,系统利用估计转速代替传感器信号作为新的反馈,保证系统的连续运行,实现了对系统的自愈控制.仿真结果验证了该方法的有效性.     

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.     

考虑多传感器故障的可重构机械臂主动取代分散容错控制

针对可重构机械臂系统位置传感器和速度传感器多故障, 提出一种主动取代分散容错控制方法. 基于可重构机械臂的模块化属性, 设计正常工作模式下的分散神经网络控制器. 利用微分同胚原理将子系统结构进行非线性变换, 将传感器故障转化成伪执行器故障, 设计分散滑模观测器以对多传感器故障进行实时检测, 并利用其输出信号取代故障传感器信号, 实现了多传感器故障情形下可重构机械臂的主动容错控制. 仿真结果表明了所设计的容错控制方法的有效性.     

光伏微逆变器电流传感器和功率管开路故障诊断

当电流传感器出现性能蜕化、故障或失效时,光伏微逆变器系统的输出会受到严重影响,甚至微逆变器系统其他部件有可能被直接损坏而导致整个系统永久失效;微逆变器系统中反激式变换器功率管的开路故障会引起2个交错支路电流不平衡,导致输出电流波形畸变率变大.为此,提出一种基于状态观测器的光伏微逆变器电流传感器和功率管开路故障诊断方法.建立两路反激式变换器的数学模型;构建状态观测器以实现对两路反激式变换器原边电流的在线估计,并生成残差;将残差与阈值进行比较,实现对微逆变器系统中电流传感器与功率管的实时故障诊断.仿真结果验证该方法可行且有效.     

基于滑模观测器和神经网络的传感器故障诊断方法比较研究

针对航空发动机传感器故障诊断中各种方法的优势和劣势,选择滑模观测器和神经网络这两种故障诊断方法分别对航空发动机转速传感器进行故障诊断研究,采用实验室搭建的发动机实验台DGEN380的实验数据,选择对航空发动机控制系统影响较大的偏置故障、漂移故障、脉冲故障、周期性干扰故障这四类传感器故障进行诊断。研究结果表明,滑模观测器和IPSO-BP神经网络都能实现航空发动机传感器的故障诊断;滑模观测器方法可以诊断出偏置故障、脉冲故障和周期性干扰故障,但不能诊断出传感器发生的漂移故障;IPSO-BP神经网络方法可以诊断出偏置故障、漂移故障、脉冲故障和周期性干扰故障。因此,滑模观测器在故障诊断中可能会出现漏诊的现象,IPSO-BP神经网络相对滑模观测器而言不会出现漏诊的现象。     

Development and application of sliding mode LPV fault reconstruction schemes for the ADDSAFE Benchmark

This paper describes the development and the evaluation of a robust sliding mode observer fault detection scheme applied to an aircraft benchmark problem as part of the ADDSAFE project. The ADDSAFE benchmark problem which is considered in this paper is the yaw rate sensor fault scenario. A robust sliding mode sensor fault reconstruction scheme based on an LPV model is presented, where the fault reconstruction signal is obtained from the so-called equivalent output error injection signal associated with the observer. The development process includes implementing the design using AIRBUS׳s the so-called SAO library which allows the automatic generation of flight certifiable code which can be implemented on the actual flight control computer. The proposed scheme has been subjected to various tests and evaluations on the Functional Engineering Simulator conducted by the industrial partners associated with the ADDSAFE project. These were designed to cover a wide range of the flight envelope, specific challenging manoeuvres and realistic fault types. The detection and isolation logic together with a statistical assessment of the FDD schemes are also presented. Simulation results from various levels of FDD developments (from tuning, testing and industrial evaluation) show consistently good results and fast detection times.     

Model predictive control-based fault detection and reconstruction algorithm for longitudinal control of autonomous driving vehicle using multi-sliding mode observer

This paper presents a model predictive control-based fault detection and reconstruction algorithm for longitudinal control of autonomous driving using a multi-sliding mode observer. In order to secure the safe longitudinal control of a vehicle, a numbers of factors must be ensured, such as the reliability of the longitudinal information, the data on the forward object from the environment sensor, and the acceleration of the ego vehicle. Thus, we propose a reasonable failure detection scheme for the acceleration signal of the host vehicle and the relative values of the front object of the radar. In order to identify the faults of the radar and the vehicle acceleration sensor related to the automated longitudinal control, the multiple sliding mode observer and prediction of model predictive control (MPC) algorithm are applied. The relative acceleration is reconstructed by applying a sliding mode observer (SMO) with clearance and relative speed measurements. The upper and lower limits of longitudinal acceleration were computed by analyzing human driving data under the preceding vehicle and reconstructed acceleration. A proper acceleration range can be defined precisely based on several reconstructed upper and lower bounds by using a multiple sliding mode observer with stored prediction data of relative values, making it possible to effectively identify the fault of the host vehicle’s acceleration sensor. By applying MPC for this study, optimal control input and prediction of relative states can be obtained that are more reasonable than those using the linear prediction model. The proposed fault detection algorithm can identify the abnormal state of the environment sensors by using the accumulated past sensor data. By comparing the stored prediction of relative states with the stored data on current states for a given period, the signal faults of the longitudinal target information can be detected from environment sensors. With these fault indices of states, the final fault diagnoses of sensors can be determined by assessing confidence through statistical analysis of 27 sets of normal driving data. In order to obtain a reasonable performance evaluation, this study uses actual driving data and a 3D full vehicle model constructed in the MATLAB/Simulink environment. The test results reveal that the proposed algorithm can successfully detect the fault of the radar and acceleration sensor of the automated driving vehicle.

     

Robust sliding mode observer-based actuator fault detection and isolation for a class of nonlinear systems

In this article, an actuator fault detection and isolation scheme for a class of nonlinear systems with uncertainty is considered. The uncertainty is allowed to have a nonlinear bound which is a general function of the state variables. A sliding mode observer is first established based on a constrained Lyapunov equation. Then, the equivalent output error injection is employed to reconstruct the fault signal using the characteristics of the sliding mode observer and the structure of the uncertainty. The reconstructed signal can approximate the system fault signal to any accuracy even in the presence of a class of uncertainty. Finally, a simulation study on a nonlinear aircraft system is presented to show the effectiveness of the scheme.     

多故障并发非线性系统一体化跟踪主动容错控制器设计

针对一类执行器及传感器同时发生故障的非线性系统,综合鲁棒滑模重构观测器及自适应滑模容错控制器设计技术,提出一体化跟踪主动容错控制方案.首先,将系统增维变换为广义系统,运用广义约束逆引入辅助矩阵,采用线性矩阵不等式设计观测器系数矩阵,综合自适应律给出广义鲁棒滑模观测器设计程式;在此基础之上,通过设计鲁棒滑模微分器估计输出向量微分,结合广义鲁棒滑模观测器状态估计结论,实现执行器及传感器故障同时重构.其次,基于故障重构及状态估计结论,提出自适应滑模的跟踪主动容错控制律设计程式.最后,通过开展飞行模拟转台伺服系统数值仿真,检验一体化跟踪主动容错控制器设计方法的有效性.     

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.     

Sensor fault diagnosis for a class of time delay uncertain nonlinear systems using neural network

In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on the output of the RBF neural network, the sliding mode observer is presented. Using the Lyapunov method, a criterion for stability is given in terms of matrix inequality. Finally, an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.