基于多模型切换的智能主动容错控制方法研究

针对某些具有可能故障先验知识的非线性系统,采用主动容错控制方法,基于神经网络对系统正常及各种先验故障情形建模,并采用基于神经网络的PID控制方法离线整定出各种故障模式下的控制律,由此建立模型库;在系统实时运行时,依据系统性能容忍度指标和模型失配度指标的计算分析,判断系统所处运行模式,切换与该模式匹配的控制律,从而达到系统主动容错控制的目的.最后将所提出的主动容错控制方法应用于某一非线性系统和线性系统,仿真结果表明了方法的有效性.     

非线性系统的主动容错控制

对一般的非线性系统提出了一种新的主动容错控制方法.系统正常工作时,采用基于迭代学习观测器的输出反馈控制策略,控制器为迭代学习观测器的状态和调节参数的函数,此输出反馈控制器能良好地镇定该非线性系统.当系统发生故障后,进行控制器重组,在调节参数的自适应调节律中引入了故障估计的信息,使得系统发生故障后包含故障估计信息的重组控制器仍然能使系统稳定,实现了非线性系统的主动容错控制.计算机模拟显示所提出算法的有效性.     

一类不确定性离散非线性重复过程的迭代学习容错控制

针对一类具有非线性和执行器故障的重复运行不确定离散系统,提出了一种迭代学习鲁棒容错控制算法.首先通过定义执行器故障系数矩阵,将迭代学习控制过程转化为等价形式的不确定性非线性重复过程模型,然后基于混合李亚普若夫函数方法讨论非线性重复过程在时间轴和批次轴两个维度上的稳定性,并以线性矩阵不等式形式给出鲁棒容错控制器存在的充分条件和设计方法,同时保证系统正常和执行器故障情形下系统的容错稳定性能.最后,单杆机械手系统的输出跟踪控制仿真结果验证了本文算法的有效性.     

基于神经网络的非线性系统故障检测及容错控制方法

利用神经网络的非线性建模能力，提出了一种非线性系统的故障检测及容错控制方法。在本方法中，首先应用神经网络设计故障估计器，在线估计系统故障向量，实现故障检测；在此基础上，引入补偿控制器，消除故障对系统运行的影响，从而实现容错控制。同时基于Ｌｙａｐｕｎｏｖ方法进行了稳定性分析。     

带有非线性扰动的不确定时变广义系统的容错控制

针对基于非线性时变扰动的一类参数不确定时变广义系统,研究了当系统存在故障时的容错控制问题,首先对系统中存在的非线性扰动在假定其满足Lipschitz条件的前提下进行了线性化处理,然后针对系统中存在的故障建立起故障模型,通过构建Lyapunov方程得到了系统广义二次稳定以及鲁棒稳定的充分条件,并在系统稳定的条件下进一步得到状态反馈容错控制器,从而实现对故障系统的容错控制。最后通过一个数值算例说明所给方法的可行性。     

基于信号重构的可重构机械臂主动分散容错控制

针对可重构机械臂系统传感器故障,提出一种基于信号重构的主动分散容错控制方法. 基于可重构机械臂系统模块化属性,采用自适应模糊分散控制系统实现正常工作模式时模块关节的轨迹跟踪控制. 当在线检测出位置或速度传感器故障时,分别采用数值积分器或微分跟踪器重构相应信号,并以之代替故障信号进行反馈实现系统的主动容错控制. 此方法充分利用了冗余信息,避免了故障关节控制性能的下降对其他关节的影响. 数值仿真结果验证了所提出容错控制方法的有效性.     

基于逆系统多模型内模主动容错控制

针对现有非线性系统对故障容错的快速性和可靠性不足,从而导致容错失败的问题,为解决上述问题,研究了一类具有可能故障先验知识的非线性系统,提出了一种基于最小二乘支持向量机(LS-SVM)建模的逆系统多模型内模主动容错控制方法.采用 LS-SVM 对系统正常以及各种先验故障情形的逆系统进行建模,构造系统的逆模型库,根据逆系统方法将逆模型与被控系统串联形成伪线性系统,设计具有良好鲁棒性能的内模控制器.系统在线运行时,监控决策机制基于对系统性能指标的仿真计算,结果表明在无需改变内模控制器的情况下达到对非线性系统故障容错的快速性和可靠性,证明方法的有效性.     

针对传感器故障的容错控制问题的数值解

针对状态反馈闭环系统中的传感器故障容错控制问题,先基于稳定多项式分解导出该容错控制问题状态反馈闭环系统稳定的充分必要条件.在此基础上,基于相容非线性方程组数值解法,提出具有传感器故障容错控制的状态反馈律设计方法.还基于数值优化解方法,提出面向闭环系统极点配置的另一状态反馈容错控制律设计方法.计算机仿真算例表明此方法的有效性.     

数据驱动逆系统方法的多模型主动容错控制

针对非线性系统提出了一种基于数据驱动逆系统思想的多模型主动容错控制方法;该方法首先采用最小二乘支持向量机(LS-SVM)对系统正常以及各种先验故障情形建立其逆模型,并以此构造系统的基本逆模型库,实际运行时依据系统性能指标切换函数,经在线计算分析,判断系统所处的运行模式,当系统发生已知故障时,调用与之匹配的LS-SVM逆模型,使其始终与被控对象串联保持为不变的伪线性系统,并以固定的内模控制器实现非线性系统对已知故障的快速容错;当系统发生未知故障时,切换至基于数据驱动技术的无模型自适应控制器(MFAC)进行过渡容错控制,在保证系统稳定的同时利用过渡容错期间的输入输出(I/O)数据建立系统当前故障情形的逆模型并添加到模型库中,使其具有自学习能力;最后以一仿真算例验证了文中所述方法的有效性。     

一类非线性系统的输出反馈容错控制

针对一类非线性系统,研究了故障情况下基于输出反馈的容错控制问题．首先基于Zhang的自适应观测器和Lin的输出反馈控制律设计了正常系统的标称控制律,分析了该控制律的性能,并得到了故障发生后仍然采用该控制律时闭环系统状态有界的充分条件．在此基础上,设计了故障系统的容错控制律,证明了若发生的故障满足前述的充分条件,则故障系统在该容错控制律下稳定．数值仿真表明了该方法的有效性．     

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.     

A Setup for Active Fault Diagnosis

A setup for active fault diagnosis (AFD) of parametric faults in dynamic systems is formulated in this note. It is shown that it is possible to use the same setup for both open loop systems, closed-loop systems based on a nominal feedback controller as well as for closed-loop systems based on a reconfigured feedback controller. This will make the proposed AFD approach very useful in connection with fault tolerant control (FTC). The setup will make it possible to let the fault diagnosis part of the fault tolerant controller remain unchanged after a change in the feedback controller. The setup for AFD is based on the Youla–Jabr–Bongiorno–Kucera (YJBK) parameterization of all stabilizing feedback controllers and the dual YJBK parameterization. It is shown that the AFD is based directly on the dual YJBK transfer function matrix. This matrix will be named the fault signature matrix when it is used in connection with AFD.     

Fault detection for output feedback control systems with actuator stuck faults: A steady‐state‐based approach

The paper studies the fault detection problem for output feedback control systems with bounded disturbances and nonzero constant reference inputs. A steady‐state‐based approach is proposed which can be used to detect small actuator stuck faults including actuator outage (the stuck value is zero). These small stuck faults, especially the outage faults, cannot be detected effectively using the existing techniques. A dynamic output feedback controller and a weighting matrix are designed simultaneously. The dynamic output feedback controller stabilizes the closed‐loop system for both fault‐free and faulty cases and attenuates the effects of disturbances. By manipulating the steady‐state values of system states with the detection weighting matrix, a residual is then generated, through which actuator stuck faults including actuator outages can be detected effectively. Simulation results are included to demonstrate our design procedure. Copyright © 2009 John Wiley & Sons, Ltd.     

Reliable State Feedback Control Synthesis For Uncertain Linear Systems

This paper is concerned with the problem of designing reliable state‐ feedback control for a class of uncertain linear systems with norm bounded uncertainty. A procedure for designing reliable state‐feedback control is presented for the case of actuator faults that can be modeled by a scaling factor. In the design, the performance of the normal system (without fault) is optimized, as the considered system operates under the normal condition most of the time. In addition, when actuator faults occur, the closed‐loop system retains robust stability and satisfies a known quadratic performance bound. A numerical example is provided to illustrate the effectiveness of the proposed design method.     

Novel MPC‐Based Fault Tolerant Tracking Control Against Sensor Faults

The problem of active fault‐tolerant tracking control with control input and system output constraints is studied for a class of discrete‐time systems subject to sensor faults. A time‐varying fault‐tolerant observer is first developed to estimate the real system state from the faulty sensor output and control input signals. Then by using the estimated state at each time step, a model predictive control (MPC)‐based fault‐tolerant tracking control scheme is presented to guarantee the desired tracking performance and the given input and output constraints on the faulty system. In comparison with many existing fault‐tolerant MPC methods, its main contribution is that the proposed state estimator is designed by the simple and online numerical computation to tolerate the possible sensor faults, so that the regular MPC algorithm without fault information can be adopted for the online calculation of fault‐tolerant control signal. The potential recursive infeasibility and computational complexity due to the faults are avoided in the scheme. Additionally, the closed‐loop stability of the post‐fault system is discussed. Simulative results of an electric throttle control system verify the effectiveness of the proposed method.     

运行控制系统集中式容错控制方法

本文针对运行控制系统,建立了运行优化控制过程的双层结构模型.在此基础上,通过建立相应的自适应故障诊断算法,提出了保证在系统有故障和干扰时仍能渐近优化指标的集中式容错控制方法,利用李雅普诺夫稳定性理论分析了自适应故障诊断算法的构建.已证明:该方法通过调整已优化的设定值来保证在回路控制层出现故障时整个运行控制仍可收敛到其原有的优化控制效果.该方法属于非完备容错控制,仿真结果验证了所提方法的有效性.     

基于双观测器的无刷电机闭环系统故障诊断

介绍了一种基于双观测器的故障隔离方法。通过设计一个不受敏感器故障累积效应影响的特殊观测器与另一个常规观测器联合起来检测和隔离传感器及执行机构的故障。对双观测器的故障诊断方法进行了改进,并将改进算法应用于无刷直流电机闭环系统的故障检测和隔离。通过仿真结果可知,对于无刷电机典型的输入、输出型故障,用该方法可以准确定位和隔离。     

Fault diagnosis with multivariate statistical models part I: using steady state fault signatures

Multivariate statistical approaches to fault detection based on historical operating data have been found to be useful with processes having a large number of measured variables and when causal models are unavailable. For fault isolation or diagnosis they have been less powerful because of the non-causal nature of the data on which they are based. To improve the fault isolation with these methods, additional data on past faults have been used to supplement the models. A critical review of this fault isolation literature is given, and an improved approach capable of handling both simple and complex faults is presented. This approach extracts fault signatures that are vectors of movement of the fault in both the model space and the residual space. The directions of these vectors are then compared to the corresponding vector directions of known faults in the fault library. Isolation is then based on a joint plot of the angles between the vectors of the current fault and those of the known faults. Although the fault signatures are based on steady-state information, the methodology assumes that time varying disturbances due to common-cause sources are always present, and it is applied to dynamic data as soon as a fault is detected. The method is demonstrated using a simulated CSTR system with feedback control, and is shown to be effective in isolating both simple and complex faults.     

Data-driven sensor fault diagnosis systems for linear feedback control loops

This paper develops a sensor fault diagnosis (SFD) scheme for a multi-input and multi-output linear dynamic system under feedback control to identify different types of sensor faults (bias, drift and precision degradation), particularly for the incipient sensor faults. Feedback control, leading to fault propagation and disguised fault rectification, imposes the challenge on the data-driven SFD. With only available output data in closed loop, the proposed scheme comprises two stages of residual generation and residual evaluation. In the residual generation, a data-driven identification of the residual generator for the feedback control system is proposed. One class of parameters in the residual generator are estimated using process delays while another class of parameters describing the output dynamic are derived by the Bayes’ formula. The means and variances control charts of online calculated residuals are made to judge the root cause. Two case studies are performed to illustrate the effectiveness of the proposed method.