Event-triggered fault estimation and sliding mode fault-tolerant control for a class of nonlinear networked control systems

This paper is concerned with integrated event-triggered fault estimation (FE) and sliding mode fault-tolerant control (FTC) for a class of discrete-time Lipschtiz nonlinear networked control systems (NCSs) subject to actuator fault and disturbance. First, an event-triggered fault/state observer is designed to estimate the system state and actuator fault simultaneously. And then, a discrete-time sliding surface is constructed in state-estimation space. By the use of a reformulated Lipschitz property and delay system analysis method, the sliding mode dynamics and state/fault error dynamics are converted into a unified linear parameter varying (LPV) networked system model by taking into account the event-triggered scheme, actuator fault, external disturbance and network-induced delay. Based on this model and with the aid of Lyapunov–Krasovskii functional method, a delay-dependent sufficient condition is derived to guarantee the stability of the resulting closed-loop system with prescribed H_∞ performance. Furthermore, an observed-based sliding mode FTC law is synthesized to make sure the reachability of the sliding surface. Finally, simulation results are conducted to verify the effectiveness of the proposed method.     

中国企业信息技术对供应链绩效的作用分析

 基于平衡记分卡的设计思想,构建了综合测度企业物流与供应链绩效的记分卡。记分卡涉及企业战略及组织间的协调能力、计划及执行能力、物流效率、信息技术应用能力4个方面。在此基础上,设计出了调查问卷,并在中国范围内开展了大规模的企业调研。利用调研数据,比较了国有企业、外资企业、民营企业在物流与供应链绩效各在方面上的差异。此外,构建了信息技术对供应链绩效的作用路径模型,分析了信息技术实施对物流效率的影响机制,为供应链管理提出了具有启发意义的观点。     

Fault detection for a class of nonlinear networked control systems with Markov sensors assignment and random transmission delays

The paper investigates the fault detection problem for a class of nonlinear networked control systems with both communication constraints and random transmission delays. The access status of the sensors is governed by a stochastic event, which is modeled as a Markov chain taking matrix values in a certain set. The main task of this paper is to design a mode-dependent fault detection filter, such that for Markov sensors assignment, random network-induced delays and the unknown input signal, the error between the fault and the residual signal is minimized. And the resulting fault detection dynamics is formulated as an _H∞$H_{\infty }$ filtering problem of a Markov jump system. The linear matrix inequality-based sufficient conditions for the existence of the fault detection filter are obtained. Finally, two examples are given to show the effectiveness of the developed method.     

Event-based state and fault estimation for nonlinear systems with logarithmic quantization and missing measurements

This paper mainly focuses on the event-based state and fault estimation problem for a class of nonlinear systems with logarithmic quantization and missing measurements. The sensors are assumed to have different missing probabilities and a constant fault is considered here. Different from a constant threshold in existing event-triggered schemes, the threshold in this paper is varying in the state-independent condition. With resort to the state augmentation approach, a new state vector consisting of the original state vector and the fault is formed, thus the corresponding state and fault estimation problem is transmitted into the recursive filtering problem. By the stochastic analysis approach, an upper bound for the filtering error covariance is obtained, which is expressed by Riccati difference equations. Meanwhile, the filter gain matrix minimizing the trace of the filtering error covariance is also derived. The developed recursive algorithm in the current paper reflects the relationship among the upper bound of the filtering error covariance, the varying threshold, the linearization error, the probabilities of missing measurements and quantization parameters. Finally, two examples are utilized to verify the effectiveness of the proposed estimation algorithm.     

基于功能的petri网飞机温控系统故障诊断研究

针对飞机温控系统的故障复杂性,提出了一种基于功能的以模糊Petri网为推理算法的故障诊断方法。首先根据系统分析,推导出子系统和系统功能参数,建立了描述功能层次关系的功能模型,提高了故障诊断的直观性和有效性;然后利用模糊Petri网的快速和并行推理能力,建立了反映功能模型的故障诊断模型,定位故障功能组件,提高了诊断的快速性和精确性;最后通过实例验证了该方法的有效性。     

Fault detection of discrete-time delay Markovian jump systems with delay term modes partially available

This paper focuses on the fault detection problem for a class of discrete-time delay Markovian jump systems with delay term modes partially available. A crucial but general hypothesis considered here is there is a suitable and effective detector to provide a measurement signal of operation mode of delay term. The fault detection filter used as the residual generator could depend on the original system operation mode or the signal emitted from detector. Via minimizing the error between the residual and fault signal, the problem of fault detection and isolation (FDI) is converted into an H_∞ filtering problem and closely related to a probability representing the degree of dependence between the original and measurable signals. An improved Lyapunov function depending on such two operation modes is exploited to study the corresponding problems. Sufficient conditions for the existence of the desired FDI filter are presented in terms of LMIs. When such a probability is uncertain or partially unknown, similar problems are also considered. A practical example is used to demonstrate the effectiveness and superiority of the proposed methods.     

Event-triggered non-fragile H∞ fault detection for discrete time-delayed nonlinear systems with channel fadings

In this paper, the event-triggered non-fragile H_∞ fault detection filter is designed for a class of discrete-time nonlinear systems subject to time-varying delays and channel fadings. The Lth Rice fading model is utilized to reflect the actual received measurement signals, and its channel coefficients own arbitrary probability density functions on interval [0,1]. The event-based filter is constructed to reduce unnecessary data transmissions in the communication channel, which only updates the measurement signal to the filter when the prespecified “event” is triggered. Multiplicative gain variations are utilized to describe the phenomenon of parameter variations in actual implementation of the filter. Based on Lyapunov stability theory, stochastic analysis technology along with linear matrix inequalities (LMIs) skills, sufficient conditions for the existence of the non-fragile fault detection filter are obtained which make the filtering error system stochastically stable and satisfy the H_∞ constraint. The gains of the filter can be calculated out by solving the feasible solution to a certain LMI. A simulation example is given to show the effectiveness of the proposed method.     

Active fault diagnosis for a class of closed-loop systems via parameter estimation

This paper addresses an active fault diagnosis problem for a class of discrete-time closed-loop system with stochastic noise. By introducing the theories of system identification, a novel active fault diagnosis method is developed to detect and isolate the faults. An important advantage of the proposed method is that there is no need to cut off the original input signal, which is necessary in most active fault diagnosis methods. Firstly, due to the features of the faults, we transform the problem of fault diagnosis into a problem of model selection by estimating model parameters. Then, the sufficient condition for active fault diagnosability is analysed, and the property that auxiliary input signal can enhance the fault diagnosability is given. Finally, simulation studies are carried out to demonstrate the effectiveness and applicability of the proposed method.     

Fault detection of discrete-time T–S fuzzy affine systems based on piecewise Lyapunov functions

This paper investigates the problem of robust fault detection for a class of discrete-time nonlinear systems, which are represented by Takagi–Sugeno (T–S) fuzzy affine dynamic models with norm-bounded uncertainties. The objective is to design an admissible fault detection filter guaranteeing the asymptotic stability of the resulting residual system with prescribed performances. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the fault detection filter implementation with state-space partition may not be synchronized with the state trajectories of the plant. Based on a piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, the results are formulated in the form of linear matrix inequalities. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approach.     

Fault reconstruction using a LPV sliding mode observer for a class of LPV systems

This paper proposes a new sliding mode observer for fault reconstruction, applicable for a class of linear parameter varying (LPV) systems. Observer schemes for actuator and sensor fault reconstruction are presented. For the actuator fault reconstruction scheme, a virtual system comprising the system matrix and a fixed input distribution matrix is used for the design of the observer. The fixed input distribution matrix is instrumental in simplifying the synthesis procedure to create the observer gains to ensure a stable closed-loop reduced order sliding motion. The ‘output error injection signals’ from the observer are used as the basis for reconstructing the fault signals. For the sensor fault observer design, augmenting the LPV system with a filtered version of the faulty measurements allows the sensor fault reconstruction problem to be posed as an actuator fault reconstruction scenario. Simulation tests based on a high-fidelity nonlinear model of a transport aircraft have been used to demonstrate the proposed actuator and sensor FDI schemes. The simulation results show their efficacy.     

Actuator fault diagnosis for a class of bilinear systems with uncertainty

In this paper, the actuator fault diagnosis problem for a class of bilinear systems with uncertainty is discussed. The system is transformed into two different subsystems. One is not affected by actuator fault, so an adaptive observer can be designed such that, under certain conditions, the observer error dynamics is stable. The other whose states can be measured is affected by the faults. The observation scheme is then used for model-based fault diagnosis. Finally, an example of a semiactive suspension system is used to illustrate the applicability of the proposed method.     

Integrated event-triggered fault estimation and fault-tolerant control for discrete-time fuzzy systems with input quantization and incomplete measurements

This paper addresses the problem of robust integrated fault estimation (FE) and fault-tolerant control (FTC) for a class of discrete-time networked Takagi–Sugeno (T–S) fuzzy systems with two-channel event-triggered schemes, input quantization and incomplete measurements. The incomplete information under consideration includes randomly occurring sensor saturation and randomly occurring quantization. In order to save the limited networked resources, this paper firstly proposed a novel dynamic event-triggered scheme on the sensor side and a static one on the controller side. Secondly, an event-triggered FE observer for the T–S fuzzy model is designed to estimate actuator faults and system states, simultaneously. Then, a specified discrete sliding surface in the state-estimation space is constructed. By using time-delay analysis technique and considering the effects of event-triggered scheme, quantization, networked conditions, actuator fault and external disturbance, the sliding mode dynamics and error dynamics are unified into a new networked time-delay model. Based on this model, sufficient conditions are established such that the resulting augmented fuzzy system is stochastically stable with a prescribed H_∞ performance level with a single-step linear matrix inequality (LMI) formulation. Furthermore, an observer-based sliding mode controller for reaching motion is synthesized to guarantee the reachability of the sliding surface. Finally, a single-link flexible manipulator example is present to illustrate the effectiveness of the proposed method.     

Robust simultaneous fault detection and control for a class of nonlinear stochastic switched delay systems under asynchronous switching

This paper concerns the simultaneous fault detection and control (SFDC) problem for a class of nonlinear stochastic switched systems with time-varying state delay and parameter uncertainties. The switching signal of detector/controller unit (DCU) is assumed to be with switching delay, which results in the asynchronous switching between the subsystems and DCU. By constructing a switching strategy depending on the state and switching delays, new sufficient conditions expressed by a set of linear matrix inequalities (LMIs) is derived to design DCU gains. This problem is formulated as an H_∞ optimization problem and both mean square exponential stability and fault detection of augmented system are considered. A numerical example is finally exploited to verify the effectiveness and potential of the achieved scheme.     

Adaptive fault-tolerant shape control for nonlinear Lipschitz stochastic distribution systems

This paper addresses the problem of adaptive fault estimation and fault-tolerant control for a class of nonlinear non-Gaussian stochastic systems subject to time-varying loss of control effectiveness faults. In this work, time-varying faults, Lipschitz nonlinear property and general stochastic characteristics are taken into consideration in a unified framework. Instead of using the system output signal, the output distribution is adopted for shape control. Both the states and faults are simultaneously estimated by an adaptive observer. Then, a fault tolerant shape controller is designed to compensate for the faults and realize stochastic output distribution tracking. Both the fault estimation and the fault tolerant control schemes are designed based on linear matrix inequality (LMI) technique. Satisfactory performance has been obtained for a numerical simulation example. Furthermore the proposed scheme is successfully tested in a case study of particle size distribution control for an emulsion polymerization reactor.     

Fault detection for discrete-time systems with randomly occurring nonlinearity and data missing: A quadrotor vehicle example

This paper concerns the fault detection (FD) problem for a class of discrete-time systems subject to data missing and randomly occurring nonlinearity modeled by two independent Bernoulli distributed random variables. We propose to design a set of fault detection filters, or residual generation systems, corresponding to each of the fault components, to guarantee that each subsystem is mean square stable and satisfies a prescribed disturbance attenuation level. Sufficient conditions are established in the form of linear matrix inequalities (LMIs). System faults can be effectively detected by generating the residues and comparing them with the dynamic fault thresholds. A quadrotor vehicle example with faults on angles and angular rates illustrates and verifies the effectiveness of the proposed algorithm.     

Observer-based fault detection for uncertain nonlinear systems

This paper addresses L₂ observer-based fault detection issues for a class of nonlinear systems in the presence of parametric and dynamic uncertainties, respectively. To this end, three different types of uncertain affine nonlinear system models studied in this paper are described first. Then, the integrated design schemes of L₂ observer-based fault detection systems are derived with the aid of Hamilton–Jacobi inequalities (HJIs), respectively. Numerical examples are also provided in the end to demonstrate the effectiveness of the proposed results.     

Fault diagnosis for a kind of nonlinear systems by using model-based contribution analysis

For the purpose of fault detection and isolation (FDI), reconstruction-based contribution (RBC) analysis is carried out in a model-based way. A bank of adaptive observers are designed for a set of potential faults. From these observers, fault estimates and fault signatures are directly available, thus contribution functions are conveniently constructed to accomplish the FDI work. This integrated design of contribution analysis and adaptive observer takes advantages of both data-driven and model-based approaches, and the diagnosis performance is improved. Furthermore, quantitative isolability analysis is also studied by similarity measurement of the obtained fault signatures. Simulation study with a nonlinear unmanned aerial vehicle (UAV) model shows the effectiveness of the proposed method.     

Composite fault-tolerant control with disturbance observer for stochastic systems with multiple disturbances

In this paper, a composite fault tolerant control (CFTC) with disturbance observer scheme is considered for a class of stochastic systems with faults and multiple disturbances. The disturbances are divided into two parts. One represents the stochastic disturbance with partial known information which is formulated by an exogenous system. The other is independent Wiener process. A stochastic disturbance observer is designed to estimate exogenous disturbance. To make the first type of disturbance can be rejected and the fault can be diagnosed, a composite fault diagnosis observer with disturbance observer is constructed. Furthermore, a composite fault-tolerant controller is proposed to compensate disturbances and faults. Finally, simulation examples are given to demonstrate the feasibility and effectiveness of the proposed scheme.     

A LPV modeling of turbocharged spark-ignition automotive engine oriented to fault detection and isolation purposes

This paper illustrates the derivation of a linear parameter varying (LPV) model approximation of a turbocharged Spark-Ignition (SI) automotive engine and its usage in designing a model-based fault detection and isolation (FDI) scheme. The LPV approximation is derived from a detailed nonlinear mathematical model of the engine on the basis of the well known Jacobian approach. The resulting LPV representation is then exploited for synthesizing a bank of LPV-FDI $H_{\infty }/H_{?}$ Luenberger observers. Each observer is in charge of detecting a particular class of fault and is designed for having low sensitivity to all other exogenous inputs so as to allow an effective fault isolation. The adopted FDI scheme is gain-scheduled and exploits a set of engine variables, assumed to be measurable on-line, as a scheduling parameters. The goodness of the LPV approximation of the engine model and the effectiveness of the LPV-FDI architecture are demonstrated by several numerical simulations.     

基于单片机的智能电力系统故障录波仪浅析

智能电力系统故障录波仪是一种数据采集记录装置,它可以记录系统非正常和正常状况下系统电压、电流、频率的变化。在电力系统正常运行情况下记录的数据,对于分析电力系统正常运行下电能的应用情况起着重要的作用;而故障阶段记录的数据,对于分析电力系统故障发生的原因,以及帮助寻找故障发生点,从而迅速处理相关故障事故起着关键的作用。