An output delay approach to fault estimation for sampled-data systems

The problem of fault estimation for a class of non-uniformly sampled-data systems is investigated from the time delay point of view in this paper.Firstly,the output delay approach is employed to model the sampled-data system as a continuous-time one with time-varying delay output.Then,based on the analysis of the inapplicability of the adaptive fault diagnosis observer in such class of time-delay systems,a novel augmented fault estimation observer design method is proposed to guarantee the exponential convergence of the estimation errors.Furthermore,an extension to the case of time varying fault estimation for the noisy sampled-data systems is studied.Finally,simulation results of a flight control system are presented to demonstrate the effectiveness of the proposed method.     

Robust fault diagnosis for a class of nonlinear systems

Robust fault diagnosis based on adaptive observer is studied for a class of nonlinear systems up to output injection. Adaptive fault updating laws are designed to guarantee the stability of the diagnosis system. The upper bounds of the state estimation error and fault estimation error of the adaptive observer are given respectively and the effects of parameter in the adaptive updating laws on fault estimation accuracy are also discussed. Simulation example demonstrates the effectiveness of the proposed methods and the analysis results.     

Robust fault diagnosis for a class of nonlinear systems

Robust fault diagnosis based on adaptive observer is studied for a class of nonlinear systems up to output injection. Adaptive fault updating laws are designed to guarantee the stability of the diagnosis system. The upper bounds of the state estimation error and fault estimation error of the adaptive observer are given respectively and the effects of parameter in the adaptive updating laws on fault estimation accuracy are also discussed. Simulation example demonstrates the effectiveness of the proposed methods and the analysis results.     

基于自适应观测器的时滞系统执行器故障诊断

该文研究了一类含有未知输人干扰和模型不确定性的线性时滞系统的故障诊断问题。通过设计自适应诊断观测器，得到了一种新型的鲁棒执行器故障诊断方法。首先针对确定性系统分别设计了检测观测器和自适应诊断观测器，前者能够检测出故障的发生，后者能够理想地估计出故障随时间变化的形状。然后考虑系统的外部干扰和模型不确定，改进了自适应诊断观测器的算法，证明了故障诊断系统的稳定性，提高了故障诊断系统的鲁棒性。最后给出了故障检测过程中阈值的选取原则。仿真结果表明算法具有良好的诊断性能。     

基于降维观测器的非最小相位系统的快速故障估计

针对传统的基于自适应观测器的故障诊断方法不适用于非最小相位系统的问题,本文基于降维 观测器提出了一种新的自适应故障估计方法．首先引入特殊坐标基（special coordinate basis, SCB）变换,可以 方便地求出误差系统的不变零点．其次对于变换之后的系统,基于降维观测器提出了一种新的快速故障估计 方法,目的在于提高故障估计的性能,即故障估计的快速性和准确性．最后仿真验证了此方法的有效性．     

Dynamic surface active fault tolerant control design for the attitude control systems of UAV with actuator fault

In this paper, an active fault tolerant control (FTC) approach based on transient performance index is proposed for the attitude control systems of unmanned aerial vehicle (UAV) with actuator fault. The nonlinear attitude control system model for UAV with actuator faults is given, which represents the dynamic characteristics of UAV. A fault diagnosis component is used for fault detection and estimation. According to the fault estimation information obtained during the fault diagnosis, the fault tolerant control scheme is developed by adopting the adaptive dynamic surface control technique, which guarantees the asymptotic output tracking and ultimate uniform boundedness of the closed-loop attitude control systems of UAV in actuator faulty case. Further, a prescribed transient performance of the FTC attitude control systems is considered which characterizes the convergence rate and maximum overshoot of the attitude tracking error. Finally, simulation results are shown that the attitude control system states remain bounded and the output tracking errors converge to a neighborhood of zero.     

一种新的基于自适应观测器中立时滞系统故障估计的设计方法

研究了基于自适应观测器中立时滞系统的故障估计问题. 首先, 本文提出了一种新的快速自适应故障估计算法提升了故障估计的快速性和准确性. 同时, 一个时滞相关的判据用于减少设计过程中的保守性, 特别对于小时滞系统. 然后, 应用线性矩阵不等式技巧, 给出了详细的设计步骤. 最后, 仿真结果验证了所提方法的有效性.     

Design of a fault diagnosis scheme for a class of singular nonlinear systems

A new fault detection and diagnosis approach is developed in this paper for a class of singular nonlinear systems via the use of adaptive updating rules. Both detection and diagnostic observers are established, where Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the process faults. This has led to stable observation error systems for both fault detection and diagnosis. A simulated numerical example is included to demonstrate the use of the proposed approach and encouraging results have been obtained.     

Design of a fault diagnosis scheme for a class of singular nonlinear systems

A new fault detection and diagnosis approach is developed in this paper for a class of singular nonlinear systems via the use of adaptive updating rules. Both detection and diagnostic observers are established, where Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the process faults. This has led to stable observation error systems for both fault detection and diagnosis. A simulated numerical example is included to demonstrate the use of the proposed approach and encouraging results have been obtained.     

Robust fault isolation for a class of non-linear input?output systems

The design and analysis of fault diagnosis methodologies for non-linear systems has received significant attention recently. This paper presents a robust fault isolation scheme for a class of non-linear systems with unstructured modelling uncertainty and partial state measurement. The proposed fault diagnosis architecture consists of a fault detection and approximation estimator and a bank of isolation estimators. Each isolation estimator corresponds to a particular type of fault in the fault class. A fault isolation decision scheme is presented with guaranteed performance. If at least one component of the output estimation error of a particular fault isolation estimator exceeds the corresponding adaptive threshold at some finite time, then the occurrence of that type of fault can be excluded. Fault isolation is achieved if this is valid for all but one isolation estimator. Based on the class of non-linear systems under consideration, fault isolability conditions are rigorously investigated, characterizing the class of non-linear faults that are isolable by the proposed scheme. Moreover, the non-conservativeness of the fault isolability conditions is illustrated by deriving a subclass of nonlinear systems and faults for which this condition is also necessary for fault isolability. A simulation example of a simple robotic system is used to show the effectiveness of the robust fault isolation methodology.     

Fast adaptive fault estimation and accommodation for nonlinear time‐varying delay systems

This paper studies the problem of fault estimation and accommodation for a class of nonlinear time‐varying delay systems using adaptive fault diagnosis observer (AFDO). A novel fast adaptive fault estimation algorithm that does not need the derivative of the output vector is proposed to enhance the performance of fault estimation. Meanwhile, a delay‐dependent criteria is obtained based on free weighting matrix method with the purpose of reducing the conservatism of the AFDO design. On the basis of fault estimation, an observer‐based fault‐tolerant controller is designed to guarantee the stability of the closed‐loop system. In terms of matrix inequality, we derive sufficient conditions for the existence of the adaptive observer and fault‐tolerant controller. Simulation results are presented to illustrate the efficiency of the proposed method. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Fault diagnosis for a class of active suspension systems with dynamic actuators’ faults

In this paper, a new fault diagnosis and fault-tolerant control method for a class of active suspension systems with actuator faults is proposed. The considered actuators have uncertain dynamic characteristics, which are the electromagnetic actuators made up with a motor control system and a ball screw transmission mechanism. To detect such suspension system actuator faults, dynamic fault diagnosis observers are designed for the actuators to estimate the possible faults. The actuators are analyzed to first and second order dynamic models, respectively, whose output can be measured but the rate is non-measurable. Then, the fault diagnosis method is developed for these two kinds of models to obtain the fault information. Using the fault estimation and adaptive control technique, a robust fault-tolerant controller is constructed to guarantee the performance of the rail vehicles in the faulty case. Finally, using the parameters of a practical suspension system, a simulation study is conducted to show the effectiveness of the proposed method.     

Fault Diagnosis and Sliding Mode Fault Tolerant Control for Non‐Gaussian Stochastic Distribution Control Systems Using T‐s Fuzzy Model

For the non‐Gaussian stochastic distribution control system using Takagi‐Sugeno fuzzy model, a new fault diagnosis and sliding mode fault tolerant control algorithm is presented. First, a new adaptive fault diagnosis algorithm is adopted to diagnose the fault that occurred in the system, and the observation error system is proven to be uniformly bounded. Second, the sliding mode control algorithm is used to reconfigure the controller, based on the fault estimation information. The post‐fault probability density function can still track the given distribution, leading to fault tolerant control of non‐Gaussian stochastic distribution control systems using Takagi‐Sugeno fuzzy model. Finally, simulation results show the effectiveness of the proposed method.     

Observer‐Based Adaptive Output Feedback Fault Tolerant Control for Nonlinear Hydro‐Turbine Governing System with State Delay

This paper focuses on the problem of adaptive output feedback fault tolerant control for a nonlinear hydro‐turbine governing system. A dynamic mathematical model of the system is established, which aims to investigate the dynamic performance of the model under servomotor delay and actuator faults. Then, a fault estimation adaptive observer is proposed to achieve online real‐time diagnosis of system faults. Based on the online fault estimation information, an observer‐based adaptive output feedback fault tolerant controller is designed. Furthermore, under reasonable assumptions, the results demonstrate that the closed‐loop control system can achieve global asymptotic stability by Lyapunov function. Finally, the numerical simulation results are presented to indicate the satisfaction control effectiveness of the proposed scheme.     

Fault diagnosis for high order systems based on model decomposition

Fault detection observer and fault estimation filter are the main tools for the model based fault diagnosis approach. The dimension of the observer gain normally depends on the system order and the system output dimension. The fault estimation filter traditionally has the same order as the monitored system. For high order systems, these methods have the potential problems such as parameter optimization and the real time implementation on-board for applications. In this paper, the system dynamical model is first decomposed into two subsystems. The first subsystem has a low order which is the same as the fault dimension. The other subsystem is not affected by the fault directly. With the new model structure, a fault detection approach is proposed where only the residual of the first subsystem is designed to be sensitive to the faults. The residual of the second subsystem is totally decoupled from the faults. Moreover, a lower order fault estimation filter (with the same dimension of the fault) design algorithm is investigated. In addition, the design of a static fault estimation matrix is presented for further improving the fault estimation precision. The effectiveness of the proposed method is demonstrated by a simulation example.     

非均匀采样数据系统时变故障估计与调节最优集成设计

针对一类发生连续时变故障的非均匀采样数据系统,建立了一套主动容错控制最优设计方案. 首先,为了实现基于非均匀离散采样输出对连续故障的估计,同时鉴于现有自适应故障诊断方法无法直接推广于非均匀采样数据系统,提出一种连续时间增广观测器最优设计方法,既能保证故障估计误差快速收敛同时又对外界干扰鲁棒;并且提出一个迭代算法对故障估计延迟与系统鲁棒性进行权衡;进一步地,基于所获得的故障信息,并考虑估计误差和时变故障内采样特性对容错控制带来的不利因素,设计基于状态反馈的非均匀采样容错控制器来快速恢复故障系统性能;最后,通过对四容水箱基准实例的仿真来验证所提方法的有效性.     

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.     

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.     

Collaborative Fault Tolerant Control for Non‐Gaussian Stochastic Distribution Systems Based on Adaptive Control Strategy

This paper presents a new fault tolerant controller design method for a class of interconnected non‐Gaussian stochastic distribution system with boundary conditions. In order to obtain the fault estimation value, an observer based fault detection and fault diagnosis algorithms are presented at first, then a collaborative fault tolerant controller is designed based on the adaptive control strategy. Different from most of the existing fault tolerant controllers, when fault occurs the controller need to be reconstructed is for the healthy subsystem in this paper. That is to say, the fault is compensated not by the faulty subsystem itself but by the healthy one. The proposed method is used to a simulation example for demonstration, and the effectiveness is verified.